

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 			__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							,       )  ->			None:
        '''simple docstring'''






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




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






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

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

        _lowercase     	=self.get_config()

        return config, pixel_values, labels




  def   A__					(    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							,       )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=TFSwinModel(config=lowerCAmelCase )
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
        _lowercase     	=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							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=TFSwinForMaskedImageModeling(config=lowerCAmelCase )
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(
            result.logits.shape							,       (self.batch_size, self.num_channels, self.image_size, self.image_size) )

        # test greyscale images
        _lowercase     	=1
        _lowercase     	=TFSwinForMaskedImageModeling(lowerCAmelCase )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






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

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




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        _lowercase	,	_lowercase	,	_lowercase     	=config_and_inputs
        _lowercase     	={'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFSwinModel,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False
  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=TFSwinModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       embed_dim=37 )




  def   A__					(    self )  ->			Tuple:
        '''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 )  ->			Any:
        '''simple docstring'''






        return




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=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 )  ->			Any:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

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




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase     	=['pixel_values']
              self.assertListEqual(arg_names[:1]							,       lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






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

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

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

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

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

              _lowercase     	=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							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=model_class(lowerCAmelCase )
        _lowercase     	=model(**self._prepare_for_class(lowerCAmelCase							,       lowerCAmelCase ) )
        _lowercase     	=outputs.hidden_states

        _lowercase     	=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
        _lowercase     	=to_atuple(config.patch_size )

        _lowercase     	=(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]							,       )

        _lowercase     	=outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCAmelCase )							,       lowerCAmelCase )

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

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

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




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase     	=to_atuple(self.model_tester.image_size )

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

              self.check_hidden_states_output(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






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

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

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

        for model_class in self.all_model_classes:
              _lowercase     	=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"]
              _lowercase     	=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]:
              _lowercase     	=TFSwinModel.from_pretrained(lowerCAmelCase )
              self.assertIsNotNone(lowerCAmelCase )





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




  @cached_property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






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




  @slow
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






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

        _lowercase     	=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
        _lowercase     	=image_processor(images=lowerCAmelCase							,       return_tensors='tf' )

        # forward pass
        _lowercase     	=model(lowerCAmelCase )

        # verify the logits
        _lowercase     	=tf.TensorShape((1, 1_000) )
        self.assertEqual(outputs.logits.shape							,       lowerCAmelCase )
        _lowercase     	=tf.constant([-0.0948, -0.6454, -0.0921] )
        self.assertTrue(np.allclose(outputs.logits[0, :3]							,       lowerCAmelCase							,       atol=1e-4 ) )



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


