

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  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=32							,  lowerCamelCase=2							,  lowerCamelCase=3							,  lowerCamelCase=16							,  lowerCamelCase=[1, 2, 1]							,  lowerCamelCase=[2, 2, 4]							,  lowerCamelCase=2							,  lowerCamelCase=2.0							,  lowerCamelCase=True							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase="gelu"							,  lowerCamelCase=False							,  lowerCamelCase=True							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-5							,  lowerCamelCase=True							,  lowerCamelCase=None							,  lowerCamelCase=True							,  lowerCamelCase=10							,  lowerCamelCase=8							,  lowerCamelCase=["stage1", "stage2"]							,  lowerCamelCase=[1, 2]							,  )						->       Optional[int]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      image_size
        snake_case_       						=      patch_size
        snake_case_       						=      num_channels
        snake_case_       						=      embed_dim
        snake_case_       						=      depths
        snake_case_       						=      num_heads
        snake_case_       						=      window_size
        snake_case_       						=      mlp_ratio
        snake_case_       						=      qkv_bias
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      drop_path_rate
        snake_case_       						=      hidden_act
        snake_case_       						=      use_absolute_embeddings
        snake_case_       						=      patch_norm
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      initializer_range
        snake_case_       						=      is_training
        snake_case_       						=      scope
        snake_case_       						=      use_labels
        snake_case_       						=      type_sequence_label_size
        snake_case_       						=      encoder_stride
        snake_case_       						=      out_features
        snake_case_       						=      out_indices







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        snake_case_       						=      self.get_config()

        return config, pixel_values, labels







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[Any]:
        snake_case_       						=      SwinModel(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )

        snake_case_       						=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
        snake_case_       						=      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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      SwinBackbone(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      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
        snake_case_       						=      None
        snake_case_       						=      SwinBackbone(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      SwinForMaskedImageModeling(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )
        self.parent.assertEqual(
            result.logits.shape							,  (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

        # test greyscale images
        snake_case_       						=      1
        snake_case_       						=      SwinForMaskedImageModeling(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      self.type_sequence_label_size
        snake_case_       						=      SwinForImageClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  labels=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        snake_case_       						=      1
        snake_case_       						=      SwinForImageClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

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







      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				
        )       						=      config_and_inputs
        snake_case_       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Union[str, Any]          =					(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase_	:      List[Any]          =					(
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCamelCase_	:      int          =					True

      lowerCamelCase_	:      List[Any]          =					False
      lowerCamelCase_	:      Union[str, Any]          =					False
      lowerCamelCase_	:      List[Any]          =					False







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      SwinModelTester(self   )
        snake_case_       						=      ConfigTester(self							,  config_class=lowerCamelCase							,  embed_dim=37   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        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   )						->       int:
        return







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      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   )						->       List[Any]:
        pass







      def 	lowerCAmelCase_    (     self   )						->       int:
        super().test_training_gradient_checkpointing()







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







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      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   )						->       str:
        pass







      @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""   )
      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        pass







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

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







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

          snake_case_       						=      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_       						=      True

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

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

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

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

          snake_case_       						=      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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[Any]:
        snake_case_       						=      model_class(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

        with torch.no_grad():
          snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )

        snake_case_       						=      outputs.hidden_states

        snake_case_       						=      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
        snake_case_       						=      (
            config.patch_size
            if isinstance(config.patch_size							,  collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        snake_case_       						=      (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":
          snake_case_       						=      outputs.reshaped_hidden_states
          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          snake_case_						,				snake_case_						,				snake_case_						,				snake_case_       						=      reshaped_hidden_states[0].shape
          snake_case_       						=      (
              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:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_       						=      (
            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:
          snake_case_       						=      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"]
          snake_case_       						=      True

          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_       						=      3

        snake_case_       						=      (
            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)
        )
        snake_case_       						=      (
            config.patch_size
            if isinstance(config.patch_size							,  collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

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

        for model_class in self.all_model_classes:
          snake_case_       						=      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"]
          snake_case_       						=      True
          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  (padded_height, padded_width)   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_       						=      SwinModel.from_pretrained(lowerCamelCase   )
          self.assertIsNotNone(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_       						=      _config_zero_init(lowerCamelCase   )
        for model_class in self.all_model_classes:
          snake_case_       						=      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  __lowerCamelCase				(	unittest.TestCase	):







      @cached_property
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        return (
            AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   )
            if is_vision_available()
            else None
        )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   ).to(lowerCamelCase   )
        snake_case_       						=      self.default_image_processor

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

        # forward pass
        with torch.no_grad():
          snake_case_       						=      model(**lowerCamelCase   )

        # verify the logits
        snake_case_       						=      torch.Size((1, 1000)   )
        self.assertEqual(outputs.logits.shape							,  lowerCamelCase   )
        snake_case_       						=      torch.tensor([-0.0948, -0.6454, -0.0921]   ).to(lowerCamelCase   )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]							,  lowerCamelCase							,  atol=1e-4   )   )







@require_torch
class  __lowerCamelCase				(	unittest.TestCase  ,							__snake_case	):
      lowerCamelCase_	:      Union[str, Any]          =					(SwinBackbone,) if is_torch_available() else ()
      lowerCamelCase_	:      List[Any]          =					SwinConfig







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      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   )   )