

"""simple docstring"""




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
						from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class 				lowerCamelCase  :

			def __init__(   self			:  Tuple      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[Any]=1_3      ,			__UpperCAmelCase			:  int=3_2      ,			__UpperCAmelCase			:  List[Any]=3      ,			__UpperCAmelCase			:  List[str]=[3, 3, 5]      ,			__UpperCAmelCase			:  Dict=[3_2, 1_6, 2_4]      ,			__UpperCAmelCase			:  str=[1_6, 2_4, 4_0]      ,			__UpperCAmelCase			:  Dict=[1, 1, 2]      ,			__UpperCAmelCase			:  Union[str, Any]=[1, 1, 2]      ,			__UpperCAmelCase			:  Dict=[1, 6, 6]      ,			__UpperCAmelCase			:  Optional[Any]=True      ,			__UpperCAmelCase			:  Union[str, Any]=True      ,			__UpperCAmelCase			:  Optional[Any]=3_7      ,			__UpperCAmelCase			:  Any="gelu"      ,			__UpperCAmelCase			:  Any=1_0      ,			)				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= image_size
						SCREAMING_SNAKE_CASE__									= num_channels
						SCREAMING_SNAKE_CASE__									= kernel_sizes
						SCREAMING_SNAKE_CASE__									= in_channels
						SCREAMING_SNAKE_CASE__									= out_channels
						SCREAMING_SNAKE_CASE__									= strides
						SCREAMING_SNAKE_CASE__									= num_block_repeats
						SCREAMING_SNAKE_CASE__									= expand_ratios
						SCREAMING_SNAKE_CASE__									= is_training
						SCREAMING_SNAKE_CASE__									= hidden_act
						SCREAMING_SNAKE_CASE__									= num_labels
						SCREAMING_SNAKE_CASE__									= use_labels

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      str:
						SCREAMING_SNAKE_CASE__									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

						SCREAMING_SNAKE_CASE__									= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			self.num_labels    )

						SCREAMING_SNAKE_CASE__									= self.get_config()
						return config, pixel_values, labels

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      str:
						return EfficientNetConfig(
						    num_channels=self.num_channels      ,			kernel_sizes=self.kernel_sizes      ,			in_channels=self.in_channels      ,			out_channels=self.out_channels      ,			strides=self.strides      ,			num_block_repeats=self.num_block_repeats      ,			expand_ratios=self.expand_ratios      ,			hidden_act=self.hidden_act      ,			num_labels=self.num_labels      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Dict    )				->      str:
						SCREAMING_SNAKE_CASE__									= EfficientNetModel(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						# expected last hidden states: B, C, H // 4, W // 4
						self.parent.assertEqual(
						    result.last_hidden_state.shape      ,			(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Any    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= EfficientNetForImageClassification(__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			labels=__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_labels)    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= config_and_inputs
						SCREAMING_SNAKE_CASE__									= {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						Optional[int]   						=							(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
			lowerCamelCase__				:						Dict   						=							(
			    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__				:						Optional[int]   						=							False
			lowerCamelCase__				:						Optional[int]   						=							False
			lowerCamelCase__				:						List[Any]   						=							False
			lowerCamelCase__				:						str   						=							False
			lowerCamelCase__				:						Dict   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= EfficientNetModelTester(self    )
						SCREAMING_SNAKE_CASE__									= ConfigTester(
						    self      ,			config_class=__UpperCAmelCase      ,			has_text_modality=__UpperCAmelCase      ,			hidden_size=3_7    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      List[str]:
						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			:  Dict    )				->      Optional[Any]:
						return

			@unittest.skip(reason="""EfficientNet does not use inputs_embeds"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[Any]:
						pass

			@unittest.skip(reason="""EfficientNet does not support input and output embeddings"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Dict:
						pass

			@unittest.skip(reason="""EfficientNet does not use feedforward chunking"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      str:
						pass

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      str:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__									= ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      int:
						def check_hidden_states_output(__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[int]    ):
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()

									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

									SCREAMING_SNAKE_CASE__									= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
									SCREAMING_SNAKE_CASE__									= sum(config.num_block_repeats    ) * 4
									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )

									# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]    )      ,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,			)

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__									= True
									check_hidden_states_output(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

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

									check_hidden_states_output(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase    )

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Union[str, Any]:
						pass

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      str:
						for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__									= EfficientNetModel.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )





			@is_pipeline_test
			@require_vision
			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      int:
						super().test_pipeline_image_classification()

def 		A    (       ):




			'''simple docstring'''


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





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

			@cached_property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Any:
						return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""    ) if is_vision_available() else None





			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      str:
						SCREAMING_SNAKE_CASE__									= EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""    ).to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.default_image_processor
						SCREAMING_SNAKE_CASE__									= prepare_img()
						SCREAMING_SNAKE_CASE__									= image_processor(images=__UpperCAmelCase      ,			return_tensors="""pt"""    ).to(__UpperCAmelCase    )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    )

						# verify the logits
						SCREAMING_SNAKE_CASE__									= torch.Size((1, 1_0_0_0)    )
						self.assertEqual(outputs.logits.shape      ,			__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= torch.tensor([0.0_001, 0.0_002, 0.0_002]    ).to(__UpperCAmelCase    )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,			__UpperCAmelCase      ,			atol=1e-4    )    )





"""simple docstring"""




import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			eos_token_id=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			max_new_tokens=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= text_encoding_offset
						SCREAMING_SNAKE_CASE__									= text_pad_token
						SCREAMING_SNAKE_CASE__									= eos_token_id
						SCREAMING_SNAKE_CASE__									= semantic_infer_token
						SCREAMING_SNAKE_CASE__									= semantic_vocab_size
						SCREAMING_SNAKE_CASE__									= max_input_semantic_length
						SCREAMING_SNAKE_CASE__									= semantic_rate_hz





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=0.7      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[str]=1_2_0_4_8      ,			__UpperCAmelCase			:  Optional[Any]=7_5      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  List[str]=1_2_0_5_0      ,			__UpperCAmelCase			:  Optional[Any]=2_5_6      ,			__UpperCAmelCase			:  int = 6_3_0      ,			__UpperCAmelCase			:  int = 6_0      ,			**__UpperCAmelCase			:  Any      ,			)				->      Dict:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= coarse_semantic_pad_token
						SCREAMING_SNAKE_CASE__									= coarse_rate_hz
						SCREAMING_SNAKE_CASE__									= n_coarse_codebooks
						SCREAMING_SNAKE_CASE__									= coarse_infer_token
						SCREAMING_SNAKE_CASE__									= max_coarse_input_length
						SCREAMING_SNAKE_CASE__									= max_coarse_history
						SCREAMING_SNAKE_CASE__									= sliding_window_len





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

						if coarse_acoustics_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""    )

						if fine_acoustics_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""    )

						SCREAMING_SNAKE_CASE__									= BarkSemanticGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkCoarseGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkFineGenerationConfig(**__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						return cls(
						    semantic_config=semantic_config.to_dict()      ,			coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,			fine_acoustics_config=fine_acoustics_config.to_dict()      ,			**__UpperCAmelCase      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

						SCREAMING_SNAKE_CASE__									= self.semantic_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.coarse_acoustics_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.fine_acoustics_config.to_dict()

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


