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 __snake_case							:




   def __init__(       self     :							Any			,      _lowercase     :							int			,      _lowercase     :							int=13			,      _lowercase     :							List[str]=32			,      _lowercase     :							int=2			,      _lowercase     :							Union[str, Any]=3			,      _lowercase     :							List[Any]=16			,      _lowercase     :							int=[1, 2, 1]			,      _lowercase     :							Dict=[2, 2, 4]			,      _lowercase     :							Optional[int]=2			,      _lowercase     :							Any=2.0			,      _lowercase     :							str=True			,      _lowercase     :							List[str]=0.0			,      _lowercase     :							Dict=0.0			,      _lowercase     :							Union[str, Any]=0.1			,      _lowercase     :							Any="gelu"			,      _lowercase     :							Union[str, Any]=False			,      _lowercase     :							Any=True			,      _lowercase     :							int=0.02			,      _lowercase     :							Optional[int]=1E-5			,      _lowercase     :							Optional[Any]=True			,      _lowercase     :							List[Any]=None			,      _lowercase     :							int=True			,      _lowercase     :							str=10			,      _lowercase     :							List[Any]=8			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__     				= self.get_config()

     return config, pixel_values, labels




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



     """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			,      )




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Optional[Any]			,      _lowercase     :							int			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFSwinModel(config=lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= model(lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__     				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
     SCREAMING_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       __a		(       self     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							int			,      _lowercase     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFSwinForMaskedImageModeling(config=lowerCamelCase_ )
     SCREAMING_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
     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= TFSwinForMaskedImageModeling(lowerCamelCase_ )

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




   def       __a		(       self     :							Dict			,      _lowercase     :							str			,      _lowercase     :							int			,      _lowercase     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.type_sequence_label_size
     SCREAMING_SNAKE_CASE__     				= TFSwinForImageClassification(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= model(lowerCamelCase_			,      labels=lowerCamelCase_ )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.type_sequence_label_size) )

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




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



     """simple docstring"""






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







@require_tf
class __snake_case							(		A_  ,						A_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				(
       (
           TFSwinModel,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   lowerCAmelCase_								=				(
       {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
       if is_tf_available()
       else {}
   )

   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFSwinModelTester(self )
     SCREAMING_SNAKE_CASE__     				= ConfigTester(self			,      config_class=lowerCamelCase_			,      embed_dim=37 )




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



     """simple docstring"""






     return




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






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




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






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




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_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       __a		(       self     :							Dict ):



     """simple docstring"""






     pass




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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

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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= True
       SCREAMING_SNAKE_CASE__     				= False
       SCREAMING_SNAKE_CASE__     				= True
       SCREAMING_SNAKE_CASE__     				= model_class(lowerCamelCase_ )
       SCREAMING_SNAKE_CASE__     				= model(**self._prepare_for_class(lowerCamelCase_			,      lowerCamelCase_ ) )
       SCREAMING_SNAKE_CASE__     				= outputs.attentions
       SCREAMING_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"]
       SCREAMING_SNAKE_CASE__     				= True
       SCREAMING_SNAKE_CASE__     				= config.window_size**2
       SCREAMING_SNAKE_CASE__     				= model_class(lowerCamelCase_ )
       SCREAMING_SNAKE_CASE__     				= model(**self._prepare_for_class(lowerCamelCase_			,      lowerCamelCase_ ) )
       SCREAMING_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]			,      )
       SCREAMING_SNAKE_CASE__     				= len(lowerCamelCase_ )

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

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

       SCREAMING_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       __a		(       self     :							Any			,      _lowercase     :							Any			,      _lowercase     :							Dict			,      _lowercase     :							List[Any]			,      _lowercase     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= model_class(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= model(**self._prepare_for_class(lowerCamelCase_			,      lowerCamelCase_ ) )
     SCREAMING_SNAKE_CASE__     				= outputs.hidden_states

     SCREAMING_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
     SCREAMING_SNAKE_CASE__     				= to_atuple(config.patch_size )

     SCREAMING_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]			,      )

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

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

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

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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__     				= to_atuple(self.model_tester.image_size )

     for model_class in self.all_model_classes:
       SCREAMING_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"]
       SCREAMING_SNAKE_CASE__     				= True

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




   def       __a		(       self     :							int ):



     """simple docstring"""






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

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

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

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




   @slow
   def       __a		(       self     :							Dict ):



     """simple docstring"""






     for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE__     				= TFSwinModel.from_pretrained(lowerCamelCase_ )
       self.assertIsNotNone(lowerCamelCase_ )







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




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



     """simple docstring"""






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




   @slow
   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224""" )
     SCREAMING_SNAKE_CASE__     				= self.default_image_processor

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

     # forward pass
     SCREAMING_SNAKE_CASE__     				= model(lowerCamelCase_ )

     # verify the logits
     SCREAMING_SNAKE_CASE__     				= tf.TensorShape((1, 10_00) )
     self.assertEqual(outputs.logits.shape			,      lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= 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__)
