



'''simple docstring'''






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		List[Any]     	=					self.config_class(**self.inputs_dict				)
        self.parent.assertTrue(hasattr(a_    ,"""hidden_sizes"""				)				)
        self.parent.assertTrue(hasattr(a_    ,"""num_encoder_blocks"""				)				)







class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=13    ,a_=64    ,a_=3    ,a_=4    ,a_=[2, 2, 2, 2]    ,a_=[8, 4, 2, 1]    ,a_=[16, 32, 64, 128]    ,a_=[1, 4, 8, 16]    ,a_=False    ,a_=True    ,a_="gelu"    ,a_=0.1    ,a_=0.02    ,a_=3    ,a_=None    ,)						->  List[str]:
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Dict     	=					batch_size
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[str]     	=					num_channels
        _UpperCAmelCase    :		Optional[Any]     	=					num_encoder_blocks
        _UpperCAmelCase    :		Union[str, Any]     	=					sr_ratios
        _UpperCAmelCase    :		Tuple     	=					depths
        _UpperCAmelCase    :		Tuple     	=					hidden_sizes
        _UpperCAmelCase    :		Any     	=					downsampling_rates
        _UpperCAmelCase    :		List[str]     	=					is_training
        _UpperCAmelCase    :		Any     	=					use_labels
        _UpperCAmelCase    :		str     	=					hidden_act
        _UpperCAmelCase    :		List[Any]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		Tuple     	=					initializer_range
        _UpperCAmelCase    :		List[Any]     	=					num_labels
        _UpperCAmelCase    :		Optional[Any]     	=					scope







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[str]     	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        _UpperCAmelCase    :		Optional[int]     	=					None
        if self.use_labels:
               _UpperCAmelCase    :		Dict     	=					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,self.num_labels				)

        _UpperCAmelCase    :		Union[str, Any]     	=					PoolFormerConfig(
            image_size=self.image_size    ,num_channels=self.num_channels    ,num_encoder_blocks=self.num_encoder_blocks    ,depths=self.depths    ,hidden_sizes=self.hidden_sizes    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,initializer_range=self.initializer_range    ,)

        return config, pixel_values, labels







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Tuple:
        _UpperCAmelCase    :		int     	=					PoolFormerModel(config=a_				)
        model.to(a_				)
        model.eval()
        _UpperCAmelCase    :		str     	=					model(a_				)
        _UpperCAmelCase    :		Optional[int]     	=					self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)







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







@require_torch
class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 UpperCAmelCase		      =    (
     {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		Dict     	=					PoolFormerModelTester(self				)
        _UpperCAmelCase    :		int     	=					PoolFormerConfigTester(self    ,config_class=a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        self.config_tester.run_common_tests()







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_				)







 @unittest.skip("""PoolFormer does not use inputs_embeds"""				)
 def _snake_case					(				self				)						->  Optional[int]:
        pass







 @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""				)
 def _snake_case					(				self				)						->  int:
        pass







 def _snake_case					(				self				)						->  Any:
        def check_hidden_states_output(a_    ,a_    ,a_				):
               _UpperCAmelCase    :		Optional[int]     	=					model_class(a_				)
               model.to(a_				)
               model.eval()

               with torch.no_grad():
                      _UpperCAmelCase    :		List[Any]     	=					model(**self._prepare_for_class(a_    ,a_				)				)

               _UpperCAmelCase    :		Union[str, Any]     	=					outputs.hidden_states

               _UpperCAmelCase    :		List[Any]     	=					self.model_tester.num_encoder_blocks
               self.assertEqual(len(a_				)    ,a_				)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]				)    ,[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]    ,)

        _UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCAmelCase    :		Optional[int]     	=					True
               check_hidden_states_output(a_    ,a_    ,a_				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase    :		str     	=					True

               check_hidden_states_output(a_    ,a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[int]:
        if not self.model_tester.is_training:
               return

        _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase    :		Dict     	=					True

        for model_class in self.all_model_classes:
               if model_class in get_values(a_				):
                      continue
               _UpperCAmelCase    :		Tuple     	=					model_class(a_				)
               model.to(a_				)
               model.train()
               _UpperCAmelCase    :		List[str]     	=					self._prepare_for_class(a_    ,a_    ,return_labels=a_				)
               _UpperCAmelCase    :		Optional[int]     	=					model(**a_				).loss
               loss.backward()







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					self.model_tester.prepare_config_and_inputs_for_common()

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

               _UpperCAmelCase    :		Any     	=					["""pixel_values"""]
               self.assertListEqual(arg_names[:1]    ,a_				)







 @slow
 def _snake_case					(				self				)						->  Union[str, Any]:
        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase    :		int     	=					PoolFormerModel.from_pretrained(a_				)
               self.assertIsNotNone(a_				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       return image



@require_torch
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @slow
 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		int     	=					PoolFormerImageProcessor()
        _UpperCAmelCase    :		int     	=					PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""				).to(a_				)

        _UpperCAmelCase    :		Dict     	=					image_processor(images=prepare_img()    ,return_tensors="""pt"""				).to(a_				)

        # forward pass
        with torch.no_grad():
               _UpperCAmelCase    :		int     	=					model(**a_				)

        # verify the logits
        _UpperCAmelCase    :		List[str]     	=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape    ,a_				)

        _UpperCAmelCase    :		List[str]     	=					torch.tensor([-0.6113, 0.1685, -0.0492]				).to(a_				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,a_    ,atol=1E-4				)				)










# Copyright 2022 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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__lowercase				    =      """Update an existing config file with the latest defaults while maintaining the old configuration."""


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					args.config_file
  if config_file is None and Path(__lowercase).exists():
    __lowerCAmelCase								=					default_config_file
  elif not Path(__lowercase).exists():
    raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.''')
  __lowerCAmelCase								=					load_config_from_file(__lowercase)

  if config_file.endswith('''.json'''):
    config.to_json_file(__lowercase)
  else:
    config.to_yaml_file(__lowercase)
  return config_file


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase								=					parser.add_parser('''update'''  ,				parents=__lowercase  ,				help=__lowercase  ,				formatter_class=__lowercase)
  parser.add_argument(
      '''--config_file'''  ,				default=__lowercase  ,				help=(
          '''The path to the config file to update. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )  ,				)

  parser.set_defaults(func=__lowercase)
  return parser




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					update_config(__lowercase)
  print(F'''Sucessfully updated the configuration file at {config_file}.''')
