

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  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]=13   ,						lowerCAmelCase__  :     str=32   ,						lowerCAmelCase__  :     Tuple=2   ,						lowerCAmelCase__  :     List[str]=3   ,						lowerCAmelCase__  :     Optional[int]=16   ,						lowerCAmelCase__  :     Optional[int]=[32, 64, 128]   ,						lowerCAmelCase__  :     Union[str, Any]=[1, 2, 1]   ,						lowerCAmelCase__  :     Optional[int]=[2, 2, 4]   ,						lowerCAmelCase__  :     Optional[int]=2   ,						lowerCAmelCase__  :     Dict=2.0   ,						lowerCAmelCase__  :     Dict=True   ,						lowerCAmelCase__  :     List[str]=0.0   ,						lowerCAmelCase__  :     Union[str, Any]=0.0   ,						lowerCAmelCase__  :     Optional[int]=0.1   ,						lowerCAmelCase__  :     Optional[Any]="gelu"   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						lowerCAmelCase__  :     str=True   ,						lowerCAmelCase__  :     Any=0.02   ,						lowerCAmelCase__  :     List[str]=1E-5   ,						lowerCAmelCase__  :     Optional[int]=True   ,						lowerCAmelCase__  :     Union[str, Any]=None   ,						lowerCAmelCase__  :     Union[str, Any]=True   ,						lowerCAmelCase__  :     Any=10   ,						lowerCAmelCase__  :     Optional[int]=8   ,						lowerCAmelCase__  :     List[Any]=["stage1", "stage2"]   ,						lowerCAmelCase__  :     str=[1, 2]   ,						):
									SCREAMING_SNAKE_CASE_: Tuple  		=       parent
									SCREAMING_SNAKE_CASE_: str  		=       batch_size
									SCREAMING_SNAKE_CASE_: int  		=       image_size
									SCREAMING_SNAKE_CASE_: str  		=       patch_size
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       num_channels
									SCREAMING_SNAKE_CASE_: str  		=       embed_dim
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       hidden_sizes
									SCREAMING_SNAKE_CASE_: str  		=       depths
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       num_heads
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       window_size
									SCREAMING_SNAKE_CASE_: int  		=       mlp_ratio
									SCREAMING_SNAKE_CASE_: Any  		=       qkv_bias
									SCREAMING_SNAKE_CASE_: List[str]  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: List[str]  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       drop_path_rate
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Dict  		=       use_absolute_embeddings
									SCREAMING_SNAKE_CASE_: Any  		=       patch_norm
									SCREAMING_SNAKE_CASE_: str  		=       layer_norm_eps
									SCREAMING_SNAKE_CASE_: Any  		=       initializer_range
									SCREAMING_SNAKE_CASE_: List[str]  		=       is_training
									SCREAMING_SNAKE_CASE_: Dict  		=       scope
									SCREAMING_SNAKE_CASE_: Any  		=       use_labels
									SCREAMING_SNAKE_CASE_: List[str]  		=       type_sequence_label_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       encoder_stride
									SCREAMING_SNAKE_CASE_: int  		=       out_features
									SCREAMING_SNAKE_CASE_: str  		=       out_indices



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_config()

									return config, pixel_values, labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FocalNetModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: str  		=       FocalNetBackbone(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__)

									# 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
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       None
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FocalNetBackbone(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: List[str]  		=       model(lowerCAmelCase__)

									# 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        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FocalNetForMaskedImageModeling(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.reconstruction.shape   ,						(self.batch_size, self.num_channels, self.image_size, self.image_size))

									# test greyscale images
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
									SCREAMING_SNAKE_CASE_: Any  		=       FocalNetForMaskedImageModeling(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									SCREAMING_SNAKE_CASE_: int  		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.reconstruction.shape   ,						(self.batch_size, 1, self.image_size, self.image_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.type_sequence_label_size
									SCREAMING_SNAKE_CASE_: str  		=       FocalNetForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: int  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size))

									# test greyscale images
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FocalNetForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size))




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.prepare_config_and_inputs()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: Dict  		=       {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     (
			    (
			        FocalNetModel,
			        FocalNetForImageClassification,
			        FocalNetForMaskedImageModeling,
			        FocalNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase   :							Dict										=     (
			    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase   :							int										=     False

			_UpperCAmelCase   :							List[str]										=     False
			_UpperCAmelCase   :							List[Any]										=     False
			_UpperCAmelCase   :							Optional[Any]										=     False
			_UpperCAmelCase   :							Optional[int]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FocalNetModelTester(self)
									SCREAMING_SNAKE_CASE_: str  		=       ConfigTester(self   ,						config_class=lowerCAmelCase__   ,						embed_dim=37   ,						has_text_modality=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     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        _SCREAMING_SNAKE_CASE			( self  :     int):
									return



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)



			@unittest.skip(reason="FocalNet does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									pass



			@unittest.skip(reason="FocalNet does not use feedforward chunking")
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       self.model_tester.prepare_config_and_inputs_for_common()

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:-1]:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: str  		=       inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															SCREAMING_SNAKE_CASE_: List[Any]  		=       [*signature.parameters.keys()]

															SCREAMING_SNAKE_CASE_: List[Any]  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Tuple  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

									SCREAMING_SNAKE_CASE_: List[str]  		=       outputs.hidden_states

									SCREAMING_SNAKE_CASE_: Any  		=       getattr(
									    self.model_tester   ,						"expected_num_hidden_layers"   ,						len(self.model_tester.depths) + 1)
									self.assertEqual(len(lowerCAmelCase__)   ,						lowerCAmelCase__)

									# FocalNet has a different seq_length
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (
									    config.patch_size
									    if isinstance(config.patch_size   ,						collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									SCREAMING_SNAKE_CASE_: List[str]  		=       (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_: Dict  		=       outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCAmelCase__)   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       reshaped_hidden_states[0].shape
									SCREAMING_SNAKE_CASE_: List[Any]  		=       (
									    reshaped_hidden_states[0].view(lowerCAmelCase__   ,						lowerCAmelCase__   ,						height * width).permute(0   ,						2   ,						1)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:])   ,						[num_patches, self.model_tester.embed_dim]   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_: Dict  		=       (
									    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]:
															SCREAMING_SNAKE_CASE_: List[str]  		=       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"]
															SCREAMING_SNAKE_CASE_: List[Any]  		=       True

															self.check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_: List[str]  		=       3

									SCREAMING_SNAKE_CASE_: int  		=       (
									    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)
									)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (
									    config.patch_size
									    if isinstance(config.patch_size   ,						collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes[:-1]:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       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"]
															SCREAMING_SNAKE_CASE_: Any  		=       True
															self.check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						(padded_height, padded_width))



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: str  		=       FocalNetModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_: int  		=       _config_zero_init(lowerCAmelCase__)
									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(config=lowerCAmelCase__)
															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        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									# TODO update organization
									return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny") if is_vision_available() else None




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       self.default_image_processor

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									SCREAMING_SNAKE_CASE_: Tuple  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**lowerCAmelCase__)

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







@require_torch
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     (FocalNetBackbone,) if is_torch_available() else ()
			_UpperCAmelCase   :							int										=     FocalNetConfig

			_UpperCAmelCase   :							Union[str, Any]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FocalNetModelTester(self)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(default='''automatic-speech-recognition'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''audio''': Audio()}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''transcription''': Value('''string'''	)}	)
			_UpperCAmelCase   :							str										=     "audio"
			_UpperCAmelCase   :							str										=     "transcription"



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									if self.audio_column not in features:
															raise ValueError(F"Column {self.audio_column} is not present in features.")
									if not isinstance(features[self.audio_column]   ,						lowerCAmelCase__):
															raise ValueError(F"Column {self.audio_column} is not an Audio type.")
									SCREAMING_SNAKE_CASE_: Tuple  		=       copy.deepcopy(self)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.input_schema.copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       features[self.audio_column]
									SCREAMING_SNAKE_CASE_: int  		=       input_schema
									return task_template




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return {self.audio_column: "audio", self.transcription_column: "transcription"}






