import inspect
import unittest

from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileNetVaForImageClassification, MobileNetVaModel
				from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor



class        lowercase_   (					__snake_case		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.config_class(**self.inputs_dict    )
													self.parent.assertTrue(hasattr(lowercase_				,    "tf_padding"    )    )
													self.parent.assertTrue(hasattr(lowercase_				,    "depth_multiplier"    )    )




class        lowercase_   :



							def __init__(						self				,    lowercase_				,    lowercase_=13				,    lowercase_=3				,    lowercase_=32				,    lowercase_=0.25				,    lowercase_=8				,    lowercase_=True				,    lowercase_=1_024				,    lowercase_=32				,    lowercase_="relu6"				,    lowercase_=0.1				,    lowercase_=0.02				,    lowercase_=True				,    lowercase_=True				,    lowercase_=10				,    lowercase_=None				,    ):
													_snake_case						:					Tuple    =   parent
													_snake_case						:					int    =   batch_size
													_snake_case						:					Optional[int]    =   num_channels
													_snake_case						:					Any    =   image_size
													_snake_case						:					Dict    =   depth_multiplier
													_snake_case						:					str    =   min_depth
													_snake_case						:					Optional[int]    =   tf_padding
													_snake_case						:					List[str]    =   int(last_hidden_size * depth_multiplier    )
													_snake_case						:					Optional[Any]    =   output_stride
													_snake_case						:					Optional[Any]    =   hidden_act
													_snake_case						:					int    =   classifier_dropout_prob
													_snake_case						:					Any    =   use_labels
													_snake_case						:					str    =   is_training
													_snake_case						:					int    =   num_labels
													_snake_case						:					Tuple    =   initializer_range
													_snake_case						:					Optional[int]    =   scope



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													_snake_case						:					Any    =   None
													_snake_case						:					Any    =   None
													if self.use_labels:
																			_snake_case						:					Optional[Any]    =   ids_tensor([self.batch_size]				,    self.num_labels    )
																			_snake_case						:					List[Any]    =   ids_tensor([self.batch_size, self.image_size, self.image_size]				,    self.num_labels    )

													_snake_case						:					str    =   self.get_config()

													return config, pixel_values, labels, pixel_labels



							def 	UpperCamelCase	(						self    ):
													return MobileNetVaConfig(
													    num_channels=self.num_channels				,    image_size=self.image_size				,    depth_multiplier=self.depth_multiplier				,    min_depth=self.min_depth				,    tf_padding=self.tf_padding				,    hidden_act=self.hidden_act				,    classifier_dropout_prob=self.classifier_dropout_prob				,    initializer_range=self.initializer_range				,    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					int    =   MobileNetVaModel(config=lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					Tuple    =   model(lowercase_    )
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (
													        self.batch_size,
													        self.last_hidden_size,
													        self.image_size // self.output_stride,
													        self.image_size // self.output_stride,
													    )				,    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.num_labels
													_snake_case						:					Any    =   MobileNetVaForImageClassification(lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					Optional[int]    =   model(lowercase_				,    labels=lowercase_    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.prepare_config_and_inputs()
													_snake_case    ,_snake_case    ,_snake_case    ,_snake_case						:					str    =   config_and_inputs
													_snake_case						:					List[str]    =   {"pixel_values": pixel_values}
													return config, inputs_dict




@require_torch
class        lowercase_   (					__snake_case						,			__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							_lowerCamelCase						=		(
							    {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   MobileNetVaModelTester(self    )
													_snake_case						:					List[Any]    =   MobileNetVaConfigTester(self				,    config_class=lowercase_				,    has_text_modality=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.config_tester.run_common_tests()



							@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"    )
							def 	UpperCamelCase	(						self    ):
													pass



							@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"    )
							def 	UpperCamelCase	(						self    ):
													pass



							@unittest.skip(reason="MobileNetV1 does not output attentions"    )
							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:					Tuple    =   model_class(lowercase_    )
																			_snake_case						:					Dict    =   inspect.signature(model.forward    )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			_snake_case						:					Optional[int]    =   [*signature.parameters.keys()]

																			_snake_case						:					Dict    =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													def check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    ):
																			_snake_case						:					Union[str, Any]    =   model_class(lowercase_    )
																			model.to(lowercase_    )
																			model.eval()

																			with torch.no_grad():
																									_snake_case						:					Any    =   model(**self._prepare_for_class(lowercase_				,    lowercase_    )    )

																			_snake_case						:					Dict    =   outputs.hidden_states

																			_snake_case						:					List[Any]    =   26
																			self.assertEqual(len(lowercase_    )				,    lowercase_    )

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

													for model_class in self.all_model_classes:
																			_snake_case						:					Dict    =   True
																			check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_snake_case						:					Optional[int]    =   True

																			check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowercase_    )



							@slow
							def 	UpperCamelCase	(						self    ):
													for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					int    =   MobileNetVaModel.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )




def  snake_case						()	->	List[Any]:

						'''simple docstring'''



						_snake_case						:					Tuple    =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						return image



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



							@cached_property
							def 	UpperCamelCase	(						self    ):
													return (
													    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"    ) if is_vision_available() else None
													)



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"    ).to(lowercase_    )

													_snake_case						:					Dict    =   self.default_image_processor
													_snake_case						:					Optional[Any]    =   prepare_img()
													_snake_case						:					int    =   image_processor(images=lowercase_				,    return_tensors="pt"    ).to(lowercase_    )

													# forward pass
													with torch.no_grad():
																			_snake_case						:					int    =   model(**lowercase_    )

													# verify the logits
													_snake_case						:					List[Any]    =   torch.Size((1, 1_001)    )
													self.assertEqual(outputs.logits.shape				,    lowercase_    )

													_snake_case						:					Any    =   torch.tensor([-4.1_739, -1.1_233, 3.1_205]    ).to(lowercase_    )

													self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    lowercase_				,    atol=1e-4    )    )
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													shutil.rmtree(self.tmpdirname    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    return_tensors="np"    )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )