



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    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=13  ,			snake_case__=32  ,			snake_case__=2  ,			snake_case__=3  ,			snake_case__=16  ,			snake_case__=[1, 2, 1]  ,			snake_case__=[2, 2, 4]  ,			snake_case__=2  ,			snake_case__=2.0  ,			snake_case__=True  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=0.1  ,			snake_case__="gelu"  ,			snake_case__=False  ,			snake_case__=True  ,			snake_case__=0.02  ,			snake_case__=1e-5  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=10  ,			snake_case__=8  ,			):

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

									UpperCamelCase_			= self.get_config()

									return config, pixel_values, labels

				def    _lowerCamelCase       (			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    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= TFSwinModel(config=snake_case__     )
									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
									UpperCamelCase_			= 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  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


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

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= TFSwinForMaskedImageModeling(snake_case__     )

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


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

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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




@require_tf
class    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (
				    (
				        TFSwinModel,
				        TFSwinForImageClassification,
				        TFSwinForMaskedImageModeling,
				    )
				    if is_tf_available()
				    else ()
				)
				lowercase__     =    (
				    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowercase__     =    False
				lowercase__     =    False
				lowercase__     =    False
				lowercase__     =    False

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= TFSwinModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__  ,			embed_dim=37     )

				def    _lowerCamelCase       (			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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__     )

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

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCamelCase_			= ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														UpperCamelCase_			= True
														UpperCamelCase_			= config.window_size**2
														UpperCamelCase_			= model_class(snake_case__     )
														UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

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

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

														UpperCamelCase_			= outputs.attentions

														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= model_class(snake_case__     )
									UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
									UpperCamelCase_			= outputs.hidden_states

									UpperCamelCase_			= getattr(
									    self.model_tester  ,			"expected_num_hidden_layers"  ,			len(self.model_tester.depths     ) + 1     )
									self.assertEqual(len(snake_case__     )  ,			snake_case__     )

									# Swin has a different seq_length
									UpperCamelCase_			= to_atuple(config.patch_size     )

									UpperCamelCase_			= (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]  ,			)

									UpperCamelCase_			= outputs.reshaped_hidden_states
									self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

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

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= to_atuple(self.model_tester.image_size     )

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

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

														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

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

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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




@require_vision
@require_tf
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

									# forward pass
									UpperCamelCase_			= model(snake_case__     )

									# verify the logits
									UpperCamelCase_			= tf.TensorShape((1, 1000)     )
									self.assertEqual(outputs.logits.shape  ,			snake_case__     )
									UpperCamelCase_			= tf.constant([-0.0_948, -0.6_454, -0.0_921]     )
									self.assertTrue(np.allclose(outputs.logits[0, :3]  ,			snake_case__  ,			atol=1e-4     )     )





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase :				Union[str, Any]							=logging.get_logger(__name__)


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """speech-encoder-decoder"""
				lowercase__     =    True

				def __init__(			self  ,			**snake_case__     ):

									'''simple docstring'''


									super().__init__(**snake_case__     )
									if "encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError(
														    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
														    F""" `decoder` sub-configurations are passed, but only {kwargs}"""     )

									UpperCamelCase_			= kwargs.pop("encoder"     )
									UpperCamelCase_			= encoder_config.pop("model_type"     )
									UpperCamelCase_			= kwargs.pop("decoder"     )
									UpperCamelCase_			= decoder_config.pop("model_type"     )

									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= True

				@classmethod
				def    _lowerCamelCase       (			cls  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
									UpperCamelCase_			= True
									UpperCamelCase_			= True

									return cls(encoder=encoder_config.to_dict()  ,			decoder=decoder_config.to_dict()  ,			**snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.encoder.to_dict()
									UpperCamelCase_			= self.decoder.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output
