


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__		:






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case=13 ,  snake_case=32 ,  snake_case=3 ,  snake_case=[3, 3, 5] ,  snake_case=[32, 16, 24] ,  snake_case=[16, 24, 40] ,  snake_case=[1, 1, 2] ,  snake_case=[1, 1, 2] ,  snake_case=[1, 6, 6] ,  snake_case=True ,  snake_case=True ,  snake_case=37 ,  snake_case="gelu" ,  snake_case=10 ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    parent
						UpperCamelCase__   =    batch_size
						UpperCamelCase__   =    image_size
						UpperCamelCase__   =    num_channels
						UpperCamelCase__   =    kernel_sizes
						UpperCamelCase__   =    in_channels
						UpperCamelCase__   =    out_channels
						UpperCamelCase__   =    strides
						UpperCamelCase__   =    num_block_repeats
						UpperCamelCase__   =    expand_ratios
						UpperCamelCase__   =    is_training
						UpperCamelCase__   =    hidden_act
						UpperCamelCase__   =    num_labels
						UpperCamelCase__   =    use_labels




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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

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

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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    EfficientNetModel(config=snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case       )
						# 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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    EfficientNetForImageClassification(snake_case       )
						model.to(snake_case       )
						model.eval()
						UpperCamelCase__   =    model(snake_case ,  labels=snake_case       )
						self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)       )







			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.prepare_config_and_inputs()
						UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    config_and_inputs
						UpperCamelCase__   =    {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class     lowerCamelCase__		(							UpperCAmelCase   ,		UpperCAmelCase   ,		unittest.TestCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Any					=						(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
			_UpperCamelCase						:   List[Any]					=						(
			    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase						:   Optional[int]					=						False
			_UpperCamelCase						:   Union[str, Any]					=						False
			_UpperCamelCase						:   List[Any]					=						False
			_UpperCamelCase						:   Any					=						False
			_UpperCamelCase						:   Optional[int]					=						False




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    EfficientNetModelTester(self       )
						UpperCamelCase__   =    ConfigTester(
						    self ,  config_class=snake_case ,  has_text_modality=snake_case ,  hidden_size=37       )




			def 			snake_case__			(							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 			snake_case__			(							self       ):

						'''simple docstring'''


						return




			@unittest.skip(reason="EfficientNet does not use inputs_embeds"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="EfficientNet does not support input and output embeddings"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="EfficientNet does not use feedforward chunking"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__   =    ["pixel_values"]
									self.assertListEqual(arg_names[:1] ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case ,  snake_case ,  snake_case       ):
									UpperCamelCase__   =    model_class(snake_case       )
									model.to(snake_case       )
									model.eval()

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

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

									# 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] ,  )

						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__   =    True
									check_hidden_states_output(snake_case ,  snake_case ,  snake_case       )

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

									check_hidden_states_output(snake_case ,  snake_case ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case       )




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__   =    EfficientNetModel.from_pretrained(snake_case       )
									self.assertIsNotNone(snake_case       )







			@is_pipeline_test
			@require_vision
			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						super().test_pipeline_image_classification()



def     UpperCamelCase_(			)->						Union[str, Any]:
			UpperCamelCase__   =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
			return image







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






			"""simple docstring"""






			@cached_property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return AutoImageProcessor.from_pretrained("google/efficientnet-b7"       ) if is_vision_available() else None







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"       ).to(snake_case       )

						UpperCamelCase__   =    self.default_image_processor
						UpperCamelCase__   =    prepare_img()
						UpperCamelCase__   =    image_processor(images=snake_case ,  return_tensors="pt"       ).to(snake_case       )

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

						# verify the logits
						UpperCamelCase__   =    torch.Size((1, 1000)       )
						self.assertEqual(outputs.logits.shape ,  snake_case       )

						UpperCamelCase__   =    torch.tensor([0.0001, 0.0002, 0.0002]       ).to(snake_case       )
						self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  snake_case ,  atol=1E-4       )       )






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


