"""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   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=1_3       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=3       ,     _SCREAMING_SNAKE_CASE=1_6       ,     _SCREAMING_SNAKE_CASE=[1, 2, 1]       ,     _SCREAMING_SNAKE_CASE=[2, 2, 4]       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=2.0       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE="gelu"       ,     _SCREAMING_SNAKE_CASE=False       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=0.0_2       ,     _SCREAMING_SNAKE_CASE=1E-5       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=1_0       ,     _SCREAMING_SNAKE_CASE=8       ,     )    ->	None:
							a_							:			List[str]     		=					parent
							a_							:			Union[str, Any]     		=					batch_size
							a_							:			Optional[int]     		=					image_size
							a_							:			Tuple     		=					patch_size
							a_							:			Tuple     		=					num_channels
							a_							:			Optional[Any]     		=					embed_dim
							a_							:			Tuple     		=					depths
							a_							:			Any     		=					num_heads
							a_							:			Optional[Any]     		=					window_size
							a_							:			Union[str, Any]     		=					mlp_ratio
							a_							:			Optional[int]     		=					qkv_bias
							a_							:			int     		=					hidden_dropout_prob
							a_							:			Tuple     		=					attention_probs_dropout_prob
							a_							:			Optional[Any]     		=					drop_path_rate
							a_							:			Optional[Any]     		=					hidden_act
							a_							:			Tuple     		=					use_absolute_embeddings
							a_							:			str     		=					patch_norm
							a_							:			Tuple     		=					layer_norm_eps
							a_							:			Dict     		=					initializer_range
							a_							:			Dict     		=					is_training
							a_							:			Union[str, Any]     		=					scope
							a_							:			int     		=					use_labels
							a_							:			Tuple     		=					type_sequence_label_size
							a_							:			Tuple     		=					encoder_stride







	def       A    (			self )    ->	Any:
							a_							:			Any     		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							a_							:			Optional[int]     		=					None
							if self.use_labels:
													a_							:			int     		=					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size )

							a_							:			Tuple     		=					self.get_config()

							return config, pixel_values, labels







	def       A    (			self )    ->	Dict:
							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       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_							:			Optional[int]     		=					TFSwinModel(config=_SCREAMING_SNAKE_CASE )
							a_							:			Any     		=					model(_SCREAMING_SNAKE_CASE )

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

							# test greyscale images
							a_							:			int     		=					1
							a_							:			Any     		=					TFSwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE )

							a_							:			Tuple     		=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							a_							:			Dict     		=					model(_SCREAMING_SNAKE_CASE )
							self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, 1, self.image_size, self.image_size) )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	List[Any]:
							a_							:			int     		=					self.type_sequence_label_size
							a_							:			Dict     		=					TFSwinForImageClassification(_SCREAMING_SNAKE_CASE )
							a_							:			Tuple     		=					model(_SCREAMING_SNAKE_CASE       ,     labels=_SCREAMING_SNAKE_CASE )
							self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							a_							:			Optional[Any]     		=					1
							a_							:			Union[str, Any]     		=					TFSwinForImageClassification(_SCREAMING_SNAKE_CASE )
							a_							:			Any     		=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							a_							:			Optional[int]     		=					model(_SCREAMING_SNAKE_CASE )
							self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size) )







	def       A    (			self )    ->	Tuple:
							a_							:			int     		=					self.prepare_config_and_inputs()
							a_							:			List[str]     		=					config_and_inputs
							a_							:			str     		=					{"pixel_values": pixel_values}
							return config, inputs_dict




@require_tf
class   UpperCAmelCase__				(    __lowerCamelCase,      __lowerCamelCase,      unittest.TestCase    ):


	"""simple docstring"""
	lowerCAmelCase__    :					List[str]						      =							(
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase__    :					Optional[Any]						      =							(
	    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase__    :					Any						      =							False
	lowerCAmelCase__    :					str						      =							False
	lowerCAmelCase__    :					List[Any]						      =							False
	lowerCAmelCase__    :					List[Any]						      =							False







	def       A    (			self )    ->	Tuple:
							a_							:			int     		=					TFSwinModelTester(self )
							a_							:			Any     		=					ConfigTester(self       ,     config_class=_SCREAMING_SNAKE_CASE       ,     embed_dim=3_7 )







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







	def       A    (			self )    ->	List[Any]:
							a_							:			Any     		=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	Tuple:
							a_							:			Tuple     		=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	str:
							a_							:			str     		=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )







	@unittest.skip(reason="Swin does not use inputs_embeds" )
	def       A    (			self )    ->	Dict:
							pass







	def       A    (			self )    ->	int:
							a_							:			List[Any]     		=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													a_							:			List[Any]     		=					model_class(_SCREAMING_SNAKE_CASE )
													self.assertIsInstance(model.get_input_embeddings()       ,     tf.keras.layers.Layer )
													a_							:			List[str]     		=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,     tf.keras.layers.Dense ) )







	def       A    (			self )    ->	Dict:
							a_							:			List[Any]     		=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													a_							:			List[str]     		=					model_class(_SCREAMING_SNAKE_CASE )
													a_							:			List[Any]     		=					inspect.signature(model.call )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													a_							:			Union[str, Any]     		=					[*signature.parameters.keys()]

													a_							:			List[str]     		=					["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,     _SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	Union[str, Any]:
							a_							:			int     		=					self.model_tester.prepare_config_and_inputs_for_common()
							a_							:			Tuple     		=					True

							for model_class in self.all_model_classes:
													a_							:			Optional[int]     		=					True
													a_							:			int     		=					False
													a_							:			Any     		=					True
													a_							:			Union[str, Any]     		=					model_class(_SCREAMING_SNAKE_CASE )
													a_							:			Tuple     		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) )
													a_							:			str     		=					outputs.attentions
													a_							:			Dict     		=					len(self.model_tester.depths )
													self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													a_							:			Any     		=					True
													a_							:			Any     		=					config.window_size**2
													a_							:			str     		=					model_class(_SCREAMING_SNAKE_CASE )
													a_							:			List[str]     		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) )
													a_							:			Union[str, Any]     		=					outputs.attentions
													self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

													self.assertListEqual(
													    list(attentions[0].shape[-3:] )       ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,     )
													a_							:			Optional[Any]     		=					len(_SCREAMING_SNAKE_CASE )

													# Check attention is always last and order is fine
													a_							:			List[str]     		=					True
													a_							:			Optional[Any]     		=					True
													a_							:			Union[str, Any]     		=					model_class(_SCREAMING_SNAKE_CASE )
													a_							:			List[str]     		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) )

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

													a_							:			Dict     		=					outputs.attentions

													self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	int:
							a_							:			Dict     		=					model_class(_SCREAMING_SNAKE_CASE )
							a_							:			List[Any]     		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) )
							a_							:			List[Any]     		=					outputs.hidden_states

							a_							:			List[str]     		=					getattr(
							    self.model_tester       ,     "expected_num_hidden_layers"       ,     len(self.model_tester.depths ) + 1 )
							self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

							# Swin has a different seq_length
							a_							:			str     		=					to_atuple(config.patch_size )

							a_							:			Tuple     		=					(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_							:			Dict     		=					outputs.reshaped_hidden_states
							self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

							a_							:			int     		=					reshaped_hidden_states[0].shape

							a_							:			Any     		=					tf.reshape(reshaped_hidden_states[0]       ,     (batch_size, num_channels, height * width) )
							a_							:			str     		=					tf.transpose(_SCREAMING_SNAKE_CASE       ,     (0, 2, 1) )

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







	def       A    (			self )    ->	List[str]:
							a_							:			Union[str, Any]     		=					self.model_tester.prepare_config_and_inputs_for_common()

							a_							:			Union[str, Any]     		=					to_atuple(self.model_tester.image_size )

							for model_class in self.all_model_classes:
													a_							:			str     		=					True
													self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													a_							:			List[str]     		=					True

													self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	Union[str, Any]:
							a_							:			Optional[int]     		=					self.model_tester.prepare_config_and_inputs_for_common()
							a_							:			Union[str, Any]     		=					3

							a_							:			List[str]     		=					to_atuple(self.model_tester.image_size )
							a_							:			Tuple     		=					to_atuple(config.patch_size )

							a_							:			List[str]     		=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							a_							:			int     		=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
													a_							:			Any     		=					True
													self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     (padded_height, padded_width) )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													a_							:			List[str]     		=					True
													self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     (padded_height, padded_width) )







	@slow
	def       A    (			self )    ->	List[Any]:
							for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													a_							:			Optional[int]     		=					TFSwinModel.from_pretrained(_SCREAMING_SNAKE_CASE )
													self.assertIsNotNone(_SCREAMING_SNAKE_CASE )




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


	"""simple docstring"""







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







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

							a_							:			Optional[int]     		=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
							a_							:			Any     		=					image_processor(images=_SCREAMING_SNAKE_CASE       ,     return_tensors="tf" )

							# forward pass
							a_							:			int     		=					model(_SCREAMING_SNAKE_CASE )

							# verify the logits
							a_							:			str     		=					tf.TensorShape((1, 1_0_0_0) )
							self.assertEqual(outputs.logits.shape       ,     _SCREAMING_SNAKE_CASE )
							a_							:			Optional[Any]     		=					tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1] )
							self.assertTrue(np.allclose(outputs.logits[0, :3]       ,     _SCREAMING_SNAKE_CASE       ,     atol=1E-4 ) )





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase     					=     {
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BarkFineModel',
				    'BarkSemanticModel',
				    'BarkCoarseModel',
				    'BarkModel',
				    'BarkPreTrainedModel',
				    'BarkCausalModel',
				]

if TYPE_CHECKING:
				from .configuration_bark import (
				    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BarkCoarseConfig,
				    BarkConfig,
				    BarkFineConfig,
				    BarkSemanticConfig,
				)
				from .processing_bark import BarkProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bark import (
								    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BarkCausalModel,
								    BarkCoarseModel,
								    BarkFineModel,
								    BarkModel,
								    BarkPreTrainedModel,
								    BarkSemanticModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




