
'''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 			UpperCamelCase__	:






 def __init__(      self	:List[str]						,					_A	:List[str]						,					_A	:Any=13						,					_A	:List[Any]=32						,					_A	:str=3						,					_A	:Optional[int]=[3, 3, 5]						,					_A	:Tuple=[32, 16, 24]						,					_A	:Tuple=[16, 24, 40]						,					_A	:Dict=[1, 1, 2]						,					_A	:Any=[1, 1, 2]						,					_A	:Optional[int]=[1, 6, 6]						,					_A	:Dict=True						,					_A	:Any=True						,					_A	:str=37						,					_A	:List[str]="gelu"						,					_A	:Optional[int]=10						,					)					->      str:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:str      )					->      Dict:







       '''simple docstring'''



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

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

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






 def 						lowercase_    (      self	:Optional[Any]      )					->      Any:







       '''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 						lowercase_    (      self	:int						,					_A	:Union[str, Any]						,					_A	:List[str]						,					_A	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      EfficientNetModel(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )
       # 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 						lowercase_    (      self	:Tuple						,					_A	:List[Any]						,					_A	:Union[str, Any]						,					_A	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Optional[Any]      )					->      Any:







       '''simple docstring'''



       __A													=      self.prepare_config_and_inputs()
       __A						,   __A						,   __A													=      config_and_inputs
       __A													=      {'pixel_values': pixel_values}
       return config, inputs_dict


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Optional[Any]    					=       (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       (
     {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__     :     Tuple    					=       False
 UpperCAmelCase__     :     Optional[int]    					=       False
 UpperCAmelCase__     :     str    					=       False
 UpperCAmelCase__     :     List[Any]    					=       False
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      EfficientNetModelTester(self      )
       __A													=      ConfigTester(
           self						,					config_class=_A						,					has_text_modality=_A						,					hidden_size=37      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Dict:







       '''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 						lowercase_    (      self	:Optional[Any]      )					->      List[str]:







       '''simple docstring'''



       return






 @unittest.skip(reason='EfficientNet does not use inputs_embeds'      )
 def 						lowercase_    (      self	:List[str]      )					->      Union[str, Any]:







       '''simple docstring'''



       pass






 @unittest.skip(reason='EfficientNet does not support input and output embeddings'      )
 def 						lowercase_    (      self	:Any      )					->      List[str]:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __A													=      ['pixel_values']
             self.assertListEqual(arg_names[:1]						,					_A      )






 def 						lowercase_    (      self	:List[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      List[Any]:







       '''simple docstring'''



       def check_hidden_states_output(_A	:str						,					_A	:Union[str, Any]						,					_A	:Union[str, Any]      ):
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()

             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

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

             # 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]						,					)

       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A													=      True
             check_hidden_states_output(_A						,					_A						,					_A      )

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

             check_hidden_states_output(_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_A      )






 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
 def 						lowercase_    (      self	:Optional[Any]      )					->      str:







       '''simple docstring'''



       pass






 @slow
 def 						lowercase_    (      self	:List[str]      )					->      Optional[int]:







       '''simple docstring'''



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






 @is_pipeline_test
 @require_vision
 @slow
 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       super().test_pipeline_image_classification()





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      return image


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






 @cached_property
 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



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






 @slow
 def 						lowercase_    (      self	:Tuple      )					->      int:







       '''simple docstring'''



       __A													=      EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7'      ).to(_A      )

       __A													=      self.default_image_processor
       __A													=      prepare_img()
       __A													=      image_processor(images=_A						,					return_tensors='pt'      ).to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the logits
       __A													=      torch.Size((1, 1_000)      )
       self.assertEqual(outputs.logits.shape						,					_A      )

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











# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCamelCase__			=    os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase__			=    os.path.join(hf_cache_home, "accelerate")
UpperCamelCase__			=    os.path.join(cache_dir, "default_config.yaml")
UpperCamelCase__			=    os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
			UpperCamelCase__			=    default_yaml_config_file
else:
			UpperCamelCase__			=    default_json_config_file






def 			UpperCAmelCase__     (     lowercase__ )			->   int:
						if config_file is not None:
												if not os.path.isfile(lowercase__ ):
																		raise FileNotFoundError(
																		    F"The passed configuration file `{config_file}` does not exist. "
																		    """Please pass an existing file to `accelerate launch`, or use the the default one """
																		    """created through `accelerate config` and run `accelerate launch` """
																		    """without the `--config_file` argument.""" )
						else:
												__lowercase											=					default_config_file
						with open(lowercase__    ,							"""r"""    ,							encoding="""utf-8""" ) as f:
												if config_file.endswith(""".json""" ):
																		if (
																		    json.load(lowercase__ ).get("""compute_environment"""    ,							ComputeEnvironment.LOCAL_MACHINE )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								__lowercase											=					ClusterConfig
																		else:
																								__lowercase											=					SageMakerConfig
																		return config_class.from_json_file(json_file=lowercase__ )
												else:
																		if (
																		    yaml.safe_load(lowercase__ ).get("""compute_environment"""    ,							ComputeEnvironment.LOCAL_MACHINE )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								__lowercase											=					ClusterConfig
																		else:
																								__lowercase											=					SageMakerConfig
																		return config_class.from_yaml_file(yaml_file=lowercase__ )
@dataclass
class       _lowerCAmelCase :






			"""simple docstring"""





			lowercase__   :	ComputeEnvironment
			lowercase__   :	Union[DistributedType, SageMakerDistributedType]
			lowercase__   :	str
			lowercase__   :	bool





			def 		snake_case__      (					self						:							Union[str, Any] )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					self.__dict__
									# For serialization, it's best to convert Enums to strings (or their underlying value type).
									for key, value in result.items():
															if isinstance(lowercase      ,  lowercase ):
																					__lowercase											=					value.value
															if isinstance(lowercase      ,  lowercase ) and not bool(lowercase ):
																					__lowercase											=					None
									__lowercase											=					{k: v for k, v in result.items() if v is not None}
									return result





			@classmethod
			def 		snake_case__      (					cls						:							List[str]      ,  lowercase						:							List[str]=None )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					default_json_config_file if json_file is None else json_file
									with open(lowercase      ,  """r"""      ,  encoding="""utf-8""" ) as f:
															__lowercase											=					json.load(lowercase )
									if "compute_environment" not in config_dict:
															__lowercase											=					ComputeEnvironment.LOCAL_MACHINE
									if "mixed_precision" not in config_dict:
															__lowercase											=					"""fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
									if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
									if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															__lowercase											=					config_dict.pop("""dynamo_backend""" )
															__lowercase											=					{} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
									if "use_cpu" not in config_dict:
															__lowercase											=					False
									return cls(**lowercase )





			def 		snake_case__      (					self						:							Tuple      ,  lowercase						:							str )    ->       Any:
									"""simple docstring"""







									with open(lowercase      ,  """w"""      ,  encoding="""utf-8""" ) as f:
															__lowercase											=					json.dumps(self.to_dict()      ,  indent=2      ,  sort_keys=lowercase ) + """\n"""
															f.write(lowercase )





			@classmethod
			def 		snake_case__      (					cls						:							List[str]      ,  lowercase						:							Optional[Any]=None )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					default_yaml_config_file if yaml_file is None else yaml_file
									with open(lowercase      ,  """r"""      ,  encoding="""utf-8""" ) as f:
															__lowercase											=					yaml.safe_load(lowercase )
									if "compute_environment" not in config_dict:
															__lowercase											=					ComputeEnvironment.LOCAL_MACHINE

									if "mixed_precision" not in config_dict:
															__lowercase											=					"""fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
									if isinstance(config_dict["""mixed_precision"""]      ,  lowercase ) and not config_dict["mixed_precision"]:
															__lowercase											=					"""no"""
									if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
									if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															__lowercase											=					config_dict.pop("""dynamo_backend""" )
															__lowercase											=					{} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
									if "use_cpu" not in config_dict:
															__lowercase											=					False
									return cls(**lowercase )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							Any )    ->       Tuple:
									"""simple docstring"""







									with open(lowercase      ,  """w"""      ,  encoding="""utf-8""" ) as f:
															yaml.safe_dump(self.to_dict()      ,  lowercase )







			def 		snake_case__      (					self						:							int )    ->       Optional[int]:
									"""simple docstring"""







									if isinstance(self.compute_environment      ,  lowercase ):
															__lowercase											=					ComputeEnvironment(self.compute_environment )
									if isinstance(self.distributed_type      ,  lowercase ):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																					__lowercase											=					SageMakerDistributedType(self.distributed_type )
															else:
																					__lowercase											=					DistributedType(self.distributed_type )
									if self.dynamo_config is None:
															__lowercase											=					{}





@dataclass
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	int
			lowercase__   :	int  			=      0
			lowercase__   :	int  			=      1
			lowercase__   :	Optional[str]  			=      None
			lowercase__   :	Optional[str]  			=      None
			lowercase__   :	Optional[int]  			=      None
			lowercase__   :	Optional[str]  			=      "static"
			lowercase__   :	Optional[bool]  			=      False
			lowercase__   :	str  			=      "main"

			# args for deepspeed_plugin
			lowercase__   :	dict  			=      None
			# args for fsdp
			lowercase__   :	dict  			=      None
			# args for megatron_lm
			lowercase__   :	dict  			=      None
			# args for ipex
			lowercase__   :	dict  			=      None
			# args for TPU
			lowercase__   :	bool  			=      False

			# args for TPU pods
			lowercase__   :	str  			=      None
			lowercase__   :	str  			=      None
			lowercase__   :	bool  			=      False
			lowercase__   :	bool  			=      False
			lowercase__   :	str  			=      None
			lowercase__   :	List[str]  			=      None
			lowercase__   :	List[str]  			=      None
			lowercase__   :	List[str]  			=      None

			# args for dynamo
			lowercase__   :	dict  			=      None





			def 		snake_case__      (					self						:							List[Any] )    ->       int:
									"""simple docstring"""







									if self.deepspeed_config is None:
															__lowercase											=					{}
									if self.fsdp_config is None:
															__lowercase											=					{}
									if self.megatron_lm_config is None:
															__lowercase											=					{}
									if self.ipex_config is None:
															__lowercase											=					{}
									return super().__post_init__()





@dataclass
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	str
			lowercase__   :	str
			lowercase__   :	Optional[str]  			=      None
			lowercase__   :	Optional[str]  			=      None
			lowercase__   :	str  			=      "us-east-1"
			lowercase__   :	int  			=      1
			lowercase__   :	str  			=      "all"
			lowercase__   :	str  			=      F'''accelerate-sagemaker-{num_machines}'''
			lowercase__   :	str  			=      SAGEMAKER_PYTORCH_VERSION
			lowercase__   :	str  			=      SAGEMAKER_TRANSFORMERS_VERSION
			lowercase__   :	str  			=      SAGEMAKER_PYTHON_VERSION
			lowercase__   :	str  			=      None
			lowercase__   :	str  			=      None
			lowercase__   :	dict  			=      None
			lowercase__   :	dict  			=      None





