





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  :


    '''simple docstring'''







    def __init__(					self      ,							A_      ,							A_=13      ,							A_=32      ,							A_=2      ,							A_=3      ,							A_=16      ,							A_=[1, 2, 1]      ,							A_=[2, 2, 4]      ,							A_=2      ,							A_=2.0      ,							A_=True      ,							A_=0.0      ,							A_=0.0      ,							A_=0.1      ,							A_="gelu"      ,							A_=False      ,							A_=True      ,							A_=0.02      ,							A_=1E-5      ,							A_=True      ,							A_=None      ,							A_=True      ,							A_=10      ,							A_=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    lowercase_				(					self    ):





        '''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    lowercase_				(					self    ):





        '''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    lowercase_				(					self      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= TFSwinModel(config=A_    )
        SCREAMING_SNAKE_CASE__       							= model(A_    )

        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    lowercase_				(					self      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= TFSwinForMaskedImageModeling(config=A_    )
        SCREAMING_SNAKE_CASE__       							= model(A_    )
        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(A_    )

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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.type_sequence_label_size
        SCREAMING_SNAKE_CASE__       							= TFSwinForImageClassification(A_    )
        SCREAMING_SNAKE_CASE__       							= model(A_      ,							labels=A_    )
        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(A_    )
        SCREAMING_SNAKE_CASE__       							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        SCREAMING_SNAKE_CASE__       							= model(A_    )
        self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)    )




    def    lowercase_				(					self    ):





        '''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  (     __SCREAMING_SNAKE_CASE ,    __SCREAMING_SNAKE_CASE ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		List[Any]							=  (
        (
            TFSwinModel,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase__						:		Dict							=  (
        {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase__						:		Any							=  False
    lowerCamelCase__						:		Tuple							=  False
    lowerCamelCase__						:		Union[str, Any]							=  False
    lowerCamelCase__						:		Any							=  False



    def    lowercase_				(					self    ):





        '''simple docstring'''

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



    def    lowercase_				(					self    ):





        '''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    lowercase_				(					self    ):





        '''simple docstring'''

        return



    def    lowercase_				(					self    ):





        '''simple docstring'''

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A_    )



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





        '''simple docstring'''

        pass



    def    lowercase_				(					self    ):





        '''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(A_    )
            self.assertIsInstance(model.get_input_embeddings()      ,							tf.keras.layers.Layer    )
            SCREAMING_SNAKE_CASE__       							= model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A_      ,							tf.keras.layers.Dense    )    )



    def    lowercase_				(					self    ):





        '''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(A_    )
            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]      ,							A_    )



    def    lowercase_				(					self    ):





        '''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(A_    )
            SCREAMING_SNAKE_CASE__       							= model(**self._prepare_for_class(A_      ,							A_    )    )
            SCREAMING_SNAKE_CASE__       							= outputs.attentions
            SCREAMING_SNAKE_CASE__       							= len(self.model_tester.depths    )
            self.assertEqual(len(A_    )      ,							A_    )

            # 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(A_    )
            SCREAMING_SNAKE_CASE__       							= model(**self._prepare_for_class(A_      ,							A_    )    )
            SCREAMING_SNAKE_CASE__       							= outputs.attentions
            self.assertEqual(len(A_    )      ,							A_    )

            self.assertListEqual(
                list(attentions[0].shape[-3:]    )      ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,							)
            SCREAMING_SNAKE_CASE__       							= len(A_    )

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

            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(A_    )    )

            SCREAMING_SNAKE_CASE__       							= outputs.attentions

            self.assertEqual(len(A_    )      ,							A_    )

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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= model_class(A_    )
        SCREAMING_SNAKE_CASE__       							= model(**self._prepare_for_class(A_      ,							A_    )    )
        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(A_    )      ,							A_    )

        # 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(A_    )      ,							A_    )

        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(A_      ,							(0, 2, 1)    )

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



    def    lowercase_				(					self    ):





        '''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(A_      ,							A_      ,							A_      ,							A_    )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__       							= True

            self.check_hidden_states_output(A_      ,							A_      ,							A_      ,							A_    )



    def    lowercase_				(					self    ):





        '''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(A_      ,							A_      ,							A_      ,							(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(A_      ,							A_      ,							A_      ,							(padded_height, padded_width)    )




    @slow
    def    lowercase_				(					self    ):





        '''simple docstring'''

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





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


    '''simple docstring'''







    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

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




    @slow
    def    lowercase_				(					self    ):





        '''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=A_      ,							return_tensors='''tf'''    )

        # forward pass
        SCREAMING_SNAKE_CASE__       							= model(A_    )

        # verify the logits
        SCREAMING_SNAKE_CASE__       							= tf.TensorShape((1, 10_00)    )
        self.assertEqual(outputs.logits.shape      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= tf.constant([-0.0948, -0.6454, -0.0921]    )
        self.assertTrue(np.allclose(outputs.logits[0, :3]      ,							A_      ,							atol=1E-4    )    )







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True  )							->	Tuple:
    if not is_fpa_available():
        raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''  )
    for name, module in model.named_children():
        if isinstance(lowerCAmelCase_   ,       nn.Linear  ) and to_transformer_engine and _convert_linear:
            # Return early if the linear layer weights are not multiples of 16
            if any(p % 1_6 != 0 for p in module.weight.shape  ):
                return
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= te.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       nn.LayerNorm  ) and to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= te.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.Linear  ) and not to_transformer_engine and _convert_linear:
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= nn.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.LayerNorm  ) and not to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= nn.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        else:
            convert_model(
                lowerCAmelCase_   ,       to_transformer_engine=lowerCAmelCase_   ,       _convert_linear=_convert_linear   ,       _convert_ln=_convert_ln   ,       )



def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    if not is_fpa_available():
        raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''  )
    for m in model.modules():
        if isinstance(lowerCAmelCase_   ,       (te.LayerNorm, te.Linear)  ):
            return True
    return False
