




import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
     from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor





class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     str=3   ,	SCREAMING_SNAKE_CASE__     :     Tuple=32   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=3   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=10   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=[10, 20, 30, 40]   ,	SCREAMING_SNAKE_CASE__     :     Dict=[1, 1, 2, 1]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=True   ,	SCREAMING_SNAKE_CASE__     :     Dict=True   ,	SCREAMING_SNAKE_CASE__     :     List[str]="relu"   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=3   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=None   ,	SCREAMING_SNAKE_CASE__     :     Tuple=["stage2", "stage3", "stage4"]   ,	SCREAMING_SNAKE_CASE__     :     Any=[2, 3, 4]   ,	)      ->       Any:
         lowerCAmelCase__		= parent
         lowerCAmelCase__		= batch_size
         lowerCAmelCase__		= image_size
         lowerCAmelCase__		= num_channels
         lowerCAmelCase__		= embeddings_size
         lowerCAmelCase__		= hidden_sizes
         lowerCAmelCase__		= depths
         lowerCAmelCase__		= is_training
         lowerCAmelCase__		= use_labels
         lowerCAmelCase__		= hidden_act
         lowerCAmelCase__		= num_labels
         lowerCAmelCase__		= scope
         lowerCAmelCase__		= len(snake_case__   )
         lowerCAmelCase__		= out_features
         lowerCAmelCase__		= out_indices


    def    a      (       self     :     Union[str, Any]   )      ->       Tuple:
         lowerCAmelCase__		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

         lowerCAmelCase__		= self.get_config()

         return config, pixel_values, labels


    def    a      (       self     :     str   )      ->       Tuple:
         return ResNetConfig(
             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   ,	out_features=self.out_features   ,	out_indices=self.out_indices   ,	)


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     str   )      ->       List[str]:
         lowerCAmelCase__		= ResNetModel(config=snake_case__   )
         model.to(snake_case__   )
         model.eval()
         lowerCAmelCase__		= model(snake_case__   )
         # 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    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       str:
         lowerCAmelCase__		= self.num_labels
         lowerCAmelCase__		= ResNetForImageClassification(snake_case__   )
         model.to(snake_case__   )
         model.eval()
         lowerCAmelCase__		= model(snake_case__   ,	labels=snake_case__   )
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)   )


    def    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Any:
         lowerCAmelCase__		= ResNetBackbone(config=snake_case__   )
         model.to(snake_case__   )
         model.eval()
         lowerCAmelCase__		= model(snake_case__   )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps   )   ,	len(config.out_features   )   )
         self.parent.assertListEqual(list(result.feature_maps[0].shape   )   ,	[self.batch_size, self.hidden_sizes[1], 4, 4]   )

         # verify channels
         self.parent.assertEqual(len(model.channels   )   ,	len(config.out_features   )   )
         self.parent.assertListEqual(model.channels   ,	config.hidden_sizes[1:]   )

         # verify backbone works with out_features=None
         lowerCAmelCase__		= None
         lowerCAmelCase__		= ResNetBackbone(config=snake_case__   )
         model.to(snake_case__   )
         model.eval()
         lowerCAmelCase__		= model(snake_case__   )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps   )   ,	1   )
         self.parent.assertListEqual(list(result.feature_maps[0].shape   )   ,	[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

         # verify channels
         self.parent.assertEqual(len(model.channels   )   ,	1   )
         self.parent.assertListEqual(model.channels   ,	[config.hidden_sizes[-1]]   )


    def    a      (       self     :     Optional[int]   )      ->       str:
         lowerCAmelCase__		= self.prepare_config_and_inputs()
         lowerCAmelCase__		= config_and_inputs
         lowerCAmelCase__		= {"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class  __lowerCamelCase       (    SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	,				unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      (
        (
            ResNetModel,
            ResNetForImageClassification,
            ResNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    snake_case__					=      (
        {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
        if is_torch_available()
        else {}
    )

    snake_case__					=      True
    snake_case__					=      False
    snake_case__					=      False
    snake_case__					=      False
    snake_case__					=      False


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= ResNetModelTester(self   )
         lowerCAmelCase__		= ConfigTester(self   ,	config_class=snake_case__   ,	has_text_modality=snake_case__   )


    def    a      (       self     :     Union[str, Any]   )      ->       Any:
         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    a      (       self     :     List[Any]   )      ->       Union[str, Any]:
         return


    @unittest.skip(reason="ResNet does not use inputs_embeds"   )
    def    a      (       self     :     Tuple   )      ->       int:
         pass


    @unittest.skip(reason="ResNet does not support input and output embeddings"   )
    def    a      (       self     :     Any   )      ->       Dict:
         pass


    def    a      (       self     :     Any   )      ->       List[str]:
         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs_for_common()

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

              lowerCAmelCase__		= ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,	snake_case__   )


    def    a      (       self     :     str   )      ->       Dict:
         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__   )


    def    a      (       self     :     Tuple   )      ->       Any:
         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*snake_case__   )


    def    a      (       self     :     Any   )      ->       int:
         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__		= model_class(config=snake_case__   )
              for name, module in model.named_modules():
                   if isinstance(snake_case__   ,	(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    a      (       self     :     Dict   )      ->       Union[str, Any]:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ):
              lowerCAmelCase__		= model_class(snake_case__   )
              model.to(snake_case__   )
              model.eval()

              with torch.no_grad():
                   lowerCAmelCase__		= model(**self._prepare_for_class(snake_case__   ,	snake_case__   )   )

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

              lowerCAmelCase__		= self.model_tester.num_stages
              self.assertEqual(len(snake_case__   )   ,	expected_num_stages + 1   )

              # ResNet'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 // 4, self.model_tester.image_size // 4]   ,	)

         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__		= ["basic", "bottleneck"]
         for model_class in self.all_model_classes:
              for layer_type in layers_type:
                   lowerCAmelCase__		= layer_type
                   lowerCAmelCase__		= 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"]
                   lowerCAmelCase__		= True

                   check_hidden_states_output(snake_case__   ,	snake_case__   ,	snake_case__   )


    @unittest.skip(reason="ResNet does not use feedforward chunking"   )
    def    a      (       self     :     Tuple   )      ->       List[str]:
         pass


    def    a      (       self     :     List[Any]   )      ->       int:
         lowerCAmelCase__		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__   )


    @slow
    def    a      (       self     :     Any   )      ->       int:
         for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__		= ResNetModel.from_pretrained(snake_case__   )
              self.assertIsNotNone(snake_case__   )






def 							_A   (						):
     """simple docstring"""

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





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




    """simple docstring"""

    @cached_property
    def    a      (       self     :     str   )      ->       Dict:
         return (
             AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
             if is_vision_available()
             else None
         )


    @slow
    def    a      (       self     :     Dict   )      ->       Tuple:
         lowerCAmelCase__		= ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(snake_case__   )

         lowerCAmelCase__		= self.default_image_processor
         lowerCAmelCase__		= prepare_img()
         lowerCAmelCase__		= image_processor(images=snake_case__   ,	return_tensors="pt"   ).to(snake_case__   )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__		= model(**snake_case__   )

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

         lowerCAmelCase__		= torch.tensor([-11.1_069, -9.7_877, -8.3_777]   ).to(snake_case__   )

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






@require_torch
class  __lowerCamelCase       (    SCREAMING_SNAKE_CASE__	,				unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      (ResNetBackbone,) if is_torch_available() else ()
    snake_case__					=      False
    snake_case__					=      ResNetConfig


    def    a      (       self     :     Tuple   )      ->       List[str]:
         lowerCAmelCase__		= ResNetModelTester(self   )








"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






