"""simple docstring"""


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.swin.modeling_tf_swin import (
     TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFSwinForImageClassification,
     TFSwinForMaskedImageModeling,
     TFSwinModel,
 )


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 					a					:



  def __init__(		self   :    Any						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int=13						,  lowerCamelCase_   :    List[str]=32						,  lowerCamelCase_   :    int=2						,  lowerCamelCase_   :    Union[str, Any]=3						,  lowerCamelCase_   :    List[Any]=16						,  lowerCamelCase_   :    int=[1, 2, 1]						,  lowerCamelCase_   :    Dict=[2, 2, 4]						,  lowerCamelCase_   :    Optional[int]=2						,  lowerCamelCase_   :    Any=2.0						,  lowerCamelCase_   :    str=True						,  lowerCamelCase_   :    List[str]=0.0						,  lowerCamelCase_   :    Dict=0.0						,  lowerCamelCase_   :    Union[str, Any]=0.1						,  lowerCamelCase_   :    Any="gelu"						,  lowerCamelCase_   :    Union[str, Any]=False						,  lowerCamelCase_   :    Any=True						,  lowerCamelCase_   :    int=0.02						,  lowerCamelCase_   :    Optional[int]=1E-5						,  lowerCamelCase_   :    Optional[Any]=True						,  lowerCamelCase_   :    List[Any]=None						,  lowerCamelCase_   :    int=True						,  lowerCamelCase_   :    str=10						,  lowerCamelCase_   :    List[Any]=8						,  )      -> None:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						image_size
      __a			      =						patch_size
      __a			      =						num_channels
      __a			      =						embed_dim
      __a			      =						depths
      __a			      =						num_heads
      __a			      =						window_size
      __a			      =						mlp_ratio
      __a			      =						qkv_bias
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						drop_path_rate
      __a			      =						hidden_act
      __a			      =						use_absolute_embeddings
      __a			      =						patch_norm
      __a			      =						layer_norm_eps
      __a			      =						initializer_range
      __a			      =						is_training
      __a			      =						scope
      __a			      =						use_labels
      __a			      =						type_sequence_label_size
      __a			      =						encoder_stride



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

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

      __a			      =						self.get_config()

      return config, pixel_values, labels



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> 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						,  )



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int     )      -> Union[str, Any]:
      __a			      =						TFSwinModel(config=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_     )

      __a			      =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
      __a			      =						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   :    List[Any]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Union[str, Any]     )      -> List[Any]:
      __a			      =						TFSwinForMaskedImageModeling(config=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_     )
      self.parent.assertEqual(
          result.logits.shape						,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

      # test greyscale images
      __a			      =						1
      __a			      =						TFSwinForMaskedImageModeling(lowerCamelCase_     )

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



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						self.type_sequence_label_size
      __a			      =						TFSwinForImageClassification(lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_						,  labels=lowerCamelCase_     )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)     )

      # test greyscale images
      __a			      =						1
      __a			      =						TFSwinForImageClassification(lowerCamelCase_     )
      __a			      =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
      __a			      =						model(lowerCamelCase_     )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> List[Any]:
      __a			      =						self.prepare_config_and_inputs()
      __a      ,  __a      ,  __a			      =						config_and_inputs
      __a			      =						{"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_tf
class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				List[str] 			=							(
      (
          TFSwinModel,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  A_ :				Union[str, Any] 			=							(
      {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
      if is_tf_available()
      else {}
  )

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



  def        lowerCAmelCase_				(		self   :    Dict     )      -> int:
      __a			      =						TFSwinModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  embed_dim=37     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> Dict:
      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[Any]     )      -> Any:
      return



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Any:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Optional[int]:
      __a			      =						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   :    Dict     )      -> Tuple:
      pass



  def        lowerCAmelCase_				(		self   :    str     )      -> Dict:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          __a			      =						model_class(lowerCamelCase_     )
          self.assertIsInstance(model.get_input_embeddings()						,  tf.keras.layers.Layer     )
          __a			      =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowerCamelCase_						,  tf.keras.layers.Dense     )     )



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

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

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



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

      for model_class in self.all_model_classes:
          __a			      =						True
          __a			      =						False
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						outputs.attentions
          __a			      =						len(self.model_tester.depths     )
          self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __a			      =						True
          __a			      =						config.window_size**2
          __a			      =						model_class(lowerCamelCase_     )
          __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						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]						,  )
          __a			      =						len(lowerCamelCase_     )

          # Check attention is always last and order is fine
          __a			      =						True
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )

          if hasattr(self.model_tester						,  """num_hidden_states_types"""     ):
              __a			      =						self.model_tester.num_hidden_states_types
          else:
              # also another +1 for reshaped_hidden_states
              __a			      =						2
          self.assertEqual(out_len + added_hidden_states						,  len(lowerCamelCase_     )     )

          __a			      =						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   :    Any						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict     )      -> Dict:
      __a			      =						model_class(lowerCamelCase_     )
      __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
      __a			      =						outputs.hidden_states

      __a			      =						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
      __a			      =						to_atuple(config.patch_size     )

      __a			      =						(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]						,  )

      __a			      =						outputs.reshaped_hidden_states
      self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

      __a      ,  __a      ,  __a      ,  __a			      =						reshaped_hidden_states[0].shape

      __a			      =						tf.reshape(reshaped_hidden_states[0]						,  (batch_size, num_channels, height * width)     )
      __a			      =						tf.transpose(lowerCamelCase_						,  (0, 2, 1)     )

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



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

      __a			      =						to_atuple(self.model_tester.image_size     )

      for model_class in self.all_model_classes:
          __a			      =						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"]
          __a			      =						True

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



  def        lowerCAmelCase_				(		self   :    int     )      -> Optional[int]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()
      __a			      =						3

      __a			      =						to_atuple(self.model_tester.image_size     )
      __a			      =						to_atuple(config.patch_size     )

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

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



  @slow
  def        lowerCAmelCase_				(		self   :    Dict     )      -> str:
      for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __a			      =						TFSwinModel.from_pretrained(lowerCamelCase_     )
          self.assertIsNotNone(lowerCamelCase_     )






@require_vision
@require_tf
class 					a					(				unittest.TestCase							):



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



  @slow
  def        lowerCAmelCase_				(		self   :    int     )      -> str:
      __a			      =						TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""     )
      __a			      =						self.default_image_processor

      __a			      =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      __a			      =						image_processor(images=lowerCamelCase_						,  return_tensors="""tf"""     )

      # forward pass
      __a			      =						model(lowerCamelCase_     )

      # verify the logits
      __a			      =						tf.TensorShape((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,  lowerCamelCase_     )
      __a			      =						tf.constant([-0.09_48, -0.64_54, -0.09_21]     )
      self.assertTrue(np.allclose(outputs.logits[0, :3]						,  lowerCamelCase_						,  atol=1E-4     )     )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
