


'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


__snake_case							   =  datasets.utils.logging.get_logger(__name__)
class      lowercase			(							folder_based_builder.FolderBasedBuilderConfig      ):

		"""simple docstring"""

		_a									=       None
		_a									=       None



class      lowercase			(							folder_based_builder.FolderBasedBuilder      ):

		"""simple docstring"""

		_a									=       datasets.Audio()
		_a									=       'audio'
		_a									=       AudioFolderConfig
		_a									=       42  # definition at the bottom of the script
		_a									=       AudioClassification(audio_column='audio'			,	label_column='label'      )



__snake_case							   =  [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
__snake_case							   =  AUDIO_EXTENSIONS



'''simple docstring'''
import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    FocalNetBackbone,
		    FocalNetForImageClassification,
		    FocalNetForMaskedImageModeling,
		    FocalNetModel,
		)
		from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=13					,     UpperCamelCase_=32					,     UpperCamelCase_=2					,     UpperCamelCase_=3					,     UpperCamelCase_=16					,     UpperCamelCase_=[32, 64, 128]					,     UpperCamelCase_=[1, 2, 1]					,     UpperCamelCase_=[2, 2, 4]					,     UpperCamelCase_=2					,     UpperCamelCase_=2.0					,     UpperCamelCase_=True					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.1					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=False					,     UpperCamelCase_=True					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=10					,     UpperCamelCase_=8					,     UpperCamelCase_=["stage1", "stage2"]					,     UpperCamelCase_=[1, 2]					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				parent
							UpperCamelCase__     :Dict							      =				batch_size
							UpperCamelCase__     :Tuple							      =				image_size
							UpperCamelCase__     :Any							      =				patch_size
							UpperCamelCase__     :Tuple							      =				num_channels
							UpperCamelCase__     :int							      =				embed_dim
							UpperCamelCase__     :Optional[int]							      =				hidden_sizes
							UpperCamelCase__     :List[str]							      =				depths
							UpperCamelCase__     :List[str]							      =				num_heads
							UpperCamelCase__     :Dict							      =				window_size
							UpperCamelCase__     :str							      =				mlp_ratio
							UpperCamelCase__     :List[str]							      =				qkv_bias
							UpperCamelCase__     :Optional[Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :List[Any]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :List[str]							      =				drop_path_rate
							UpperCamelCase__     :str							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				use_absolute_embeddings
							UpperCamelCase__     :str							      =				patch_norm
							UpperCamelCase__     :List[Any]							      =				layer_norm_eps
							UpperCamelCase__     :Dict							      =				initializer_range
							UpperCamelCase__     :int							      =				is_training
							UpperCamelCase__     :List[Any]							      =				scope
							UpperCamelCase__     :Dict							      =				use_labels
							UpperCamelCase__     :Dict							      =				type_sequence_label_size
							UpperCamelCase__     :Tuple							      =				encoder_stride
							UpperCamelCase__     :Optional[int]							      =				out_features
							UpperCamelCase__     :Optional[int]							      =				out_indices




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							UpperCamelCase__     :Tuple							      =				self.get_config()

							return config, pixel_values, labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return FocalNetConfig(
							    image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     embed_dim=self.embed_dim					,     hidden_sizes=self.hidden_sizes					,     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					,     out_features=self.out_features					,     out_indices=self.out_indices					,     )




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							UpperCamelCase__     :Optional[Any]							      =				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					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Any							      =				model(UpperCamelCase_							)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps							)					,     len(config.out_features							)							)
							self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,     [self.batch_size, self.image_size, 8, 8]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)					,     len(config.out_features							)							)
							self.parent.assertListEqual(model.channels					,     config.hidden_sizes[:-1]							)

							# verify backbone works with out_features=None
							UpperCamelCase__     :Tuple							      =				None
							UpperCamelCase__     :Optional[Any]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[str]							      =				model(UpperCamelCase_							)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps							)					,     1							)
							self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,     [self.batch_size, self.image_size * 2, 4, 4]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)					,     1							)
							self.parent.assertListEqual(model.channels					,     [config.hidden_sizes[-1]]							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				FocalNetForMaskedImageModeling(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.reconstruction.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

							# test greyscale images
							UpperCamelCase__     :Optional[int]							      =				1
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForMaskedImageModeling(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :str							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.reconstruction.shape					,     (self.batch_size, 1, self.image_size, self.image_size)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.type_sequence_label_size
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :int							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)

							# test greyscale images
							UpperCamelCase__     :Tuple							      =				1
							UpperCamelCase__     :Tuple							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :Optional[int]							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Dict							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.prepare_config_and_inputs()

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				config_and_inputs
							UpperCamelCase__     :Any							      =				{'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class      lowercase			(							A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (
		    (
		        FocalNetModel,
		        FocalNetForImageClassification,
		        FocalNetForMaskedImageModeling,
		        FocalNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		_a									=       (
		    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
		    if is_torch_available()
		    else {}
		)
		_a									=       False

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				FocalNetModelTester(self							)
							UpperCamelCase__     :Optional[int]							      =				ConfigTester(self					,     config_class=UpperCamelCase_					,     embed_dim=37					,     has_text_modality=UpperCamelCase_							)




		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__     :int							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_							)




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

							'''simple docstring'''


							pass




		@unittest.skip(reason='''FocalNet does not use feedforward chunking'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :str							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :Optional[Any]							      =				model_class(UpperCamelCase_							)
												self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)							)
												UpperCamelCase__     :Any							      =				model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCamelCase_					,     nn.Linear							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				model_class(UpperCamelCase_							)
												UpperCamelCase__     :Dict							      =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__     :Union[str, Any]							      =				[*signature.parameters.keys()]

												UpperCamelCase__     :Tuple							      =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				model_class(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							with torch.no_grad():
												UpperCamelCase__     :str							      =				model(**self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)							)

							UpperCamelCase__     :List[Any]							      =				outputs.hidden_states

							UpperCamelCase__     :Optional[int]							      =				getattr(
							    self.model_tester					,     '''expected_num_hidden_layers'''					,     len(self.model_tester.depths							) + 1							)
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							# FocalNet has a different seq_length
							UpperCamelCase__     :List[str]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				(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__     :List[Any]							      =				outputs.reshaped_hidden_states
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				reshaped_hidden_states[0].shape
							UpperCamelCase__     :List[str]							      =				(
							    reshaped_hidden_states[0].view(UpperCamelCase_					,     UpperCamelCase_					,     height * width							).permute(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__     :Any							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :List[str]							      =				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size					,     collections.abc.Iterable							)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :Union[str, Any]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

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

												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__     :Optional[int]							      =				3

							UpperCamelCase__     :str							      =				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size					,     collections.abc.Iterable							)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							UpperCamelCase__     :Optional[int]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							UpperCamelCase__     :Dict							      =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     (padded_height, padded_width)							)

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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :Union[str, Any]							      =				FocalNetModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :Dict							      =				_config_zero_init(UpperCamelCase_							)
							for model_class in self.all_model_classes:
												UpperCamelCase__     :int							      =				model_class(config=UpperCamelCase_							)
												for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()					,     [0.0, 1.0]					,     msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''					,     )



@require_vision
@require_torch
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''							) if is_vision_available() else None




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.default_image_processor

							UpperCamelCase__     :str							      =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__     :Any							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Tuple							      =				model(**UpperCamelCase_							)

							# verify the logits
							UpperCamelCase__     :Any							      =				torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([0.2166, -0.4368, 0.2191]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     UpperCamelCase_					,     atol=1e-4							)							)
							self.assertTrue(outputs.logits.argmax(dim=-1							).item()					,     281							)



@require_torch
class      lowercase			(							A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (FocalNetBackbone,) if is_torch_available() else ()
		_a									=       FocalNetConfig

		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModelTester(self							)