




import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, 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 torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      int=3   ,					UpperCAmelCase__ :      List[Any]=32   ,					UpperCAmelCase__ :      Any=3   ,					UpperCAmelCase__ :      Tuple=10   ,					UpperCAmelCase__ :      List[Any]=[10, 20, 30, 40]   ,					UpperCAmelCase__ :      str=[1, 1, 2, 1]   ,					UpperCAmelCase__ :      Union[str, Any]=True   ,					UpperCAmelCase__ :      List[str]=True   ,					UpperCAmelCase__ :      Any="relu"   ,					UpperCAmelCase__ :      Optional[Any]=3   ,					UpperCAmelCase__ :      Dict=None   ,					)		->Tuple:





           '''simple docstring'''

           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       image_size
           A__ 					=       num_channels
           A__ 					=       embeddings_size
           A__ 					=       hidden_sizes
           A__ 					=       depths
           A__ 					=       is_training
           A__ 					=       use_labels
           A__ 					=       hidden_act
           A__ 					=       num_labels
           A__ 					=       scope
           A__ 					=       len(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[Any]:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      str)		->List[str]:





           '''simple docstring'''

           return RegNetConfig(
               num_channels=self.num_channels   ,					embeddings_size=self.embeddings_size   ,					hidden_sizes=self.hidden_sizes   ,					depths=self.depths   ,					hidden_act=self.hidden_act   ,					num_labels=self.num_labels   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[Any])		->str:





           '''simple docstring'''

           A__ 					=       RegNetModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__ 					=       model(UpperCAmelCase__)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      List[Any])		->int:





           '''simple docstring'''

           A__ 					=       self.num_labels
           A__ 					=       RegNetForImageClassification(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__ 					=       model(UpperCAmelCase__   ,					labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels))





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''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_       (					UpperCAmelCase__						,   UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    UpperCAmelCase__				    = (
        {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Any:





           '''simple docstring'''

           A__ 					=       RegNetModelTester(self)
           A__ 					=       ConfigTester(self   ,					config_class=UpperCAmelCase__   ,					has_text_modality=UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Tuple:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           return


    @unittest.skip(reason='''RegNet does not use inputs_embeds''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''RegNet does not support input and output embeddings''')
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->str:





           '''simple docstring'''

           pass


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           A__					,				A__ 					=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__ 					=       model_class(UpperCAmelCase__)
                  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]   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->int:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


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





           '''simple docstring'''

           A__					,				A__ 					=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__ 					=       model_class(config=UpperCAmelCase__)
                  for name, module in model.named_modules():
                         if isinstance(UpperCAmelCase__   ,					(nn.BatchNormad, nn.GroupNorm)):
                                self.assertTrue(
                                    torch.all(module.weight == 1)   ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,					)
                                self.assertTrue(
                                    torch.all(module.bias == 0)   ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,					)


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





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Dict):
                  A__ 					=       model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

                  with torch.no_grad():
                         A__ 					=       model(**self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__))

                  A__ 					=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  A__ 					=       self.model_tester.num_stages
                  self.assertEqual(len(UpperCAmelCase__)   ,					expected_num_stages + 1)

                  # RegNet'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()
           A__ 					=       ['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         A__ 					=       layer_type
                         A__ 					=       True
                         check_hidden_states_output(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)

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

                         check_hidden_states_output(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Any:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)





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





           '''simple docstring'''

           for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__ 					=       RegNetModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE							(			)  ->			str:



       """simple docstring"""

       A__ 					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


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

    '''simple docstring'''






    @cached_property
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )





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





           '''simple docstring'''

           A__ 					=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(UpperCAmelCase__)

           A__ 					=       self.default_image_processor
           A__ 					=       prepare_img()
           A__ 					=       image_processor(images=UpperCAmelCase__   ,					return_tensors='''pt''').to(UpperCAmelCase__)

           # forward pass
           with torch.no_grad():
                  A__ 					=       model(**UpperCAmelCase__)

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

           A__ 					=       torch.tensor([-0.4180, -1.5051, -3.4836]).to(UpperCAmelCase__)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					UpperCAmelCase__   ,					atol=1e-4))






'''simple docstring'''



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


A__						:   List[str]				=    """3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
      print("""Torch version:""", None)

try:
      import transformers

      print("""transformers version:""", transformers.__version__)
except ImportError:
      print("""transformers version:""", None)






