




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 if gpta_config_file == "":
  UpperCAmelCase__							 =						GPTaConfig()
 else:
  UpperCAmelCase__							 =						GPTaConfig.from_json_file(_lowerCAmelCase	)
 UpperCAmelCase__							 =						GPTaModel(_lowerCAmelCase	)

 # Load weights from numpy
 load_tf_weights_in_gpta(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

 # Save pytorch-model
 UpperCAmelCase__							 =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 UpperCAmelCase__							 =						pytorch_dump_folder_path + "/" + CONFIG_NAME
 print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
 torch.save(model.state_dict()						,							_lowerCAmelCase	)
 print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
 with open(_lowerCAmelCase						,							"w"						,							encoding="utf-8"	) as f:
  f.write(config.to_json_string()	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Any		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--gpt2_config_file",
      default="",
      type=str,
      help=(
          "An optional config json file corresponding to the pre-trained OpenAI model. \n"
          "This specifies the model architecture."
      ),
  )
  _lowerCAmelCase						:   Tuple		=   parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)











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      :
      def __init__(			self   :List[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Any=3 ,      lowerCamelCase   :List[str]=32 ,      lowerCamelCase   :List[str]=3 ,      lowerCamelCase   :List[str]=10 ,      lowerCamelCase   :List[Any]=[10, 20, 30, 40] ,      lowerCamelCase   :Optional[Any]=[1, 1, 2, 1] ,      lowerCamelCase   :List[str]=True ,      lowerCamelCase   :List[Any]=True ,      lowerCamelCase   :str="relu" ,      lowerCamelCase   :Optional[Any]=3 ,      lowerCamelCase   :List[str]=None ,      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						image_size
       UpperCAmelCase__							 =						num_channels
       UpperCAmelCase__							 =						embeddings_size
       UpperCAmelCase__							 =						hidden_sizes
       UpperCAmelCase__							 =						depths
       UpperCAmelCase__							 =						is_training
       UpperCAmelCase__							 =						use_labels
       UpperCAmelCase__							 =						hidden_act
       UpperCAmelCase__							 =						num_labels
       UpperCAmelCase__							 =						scope
       UpperCAmelCase__							 =						len(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       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 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       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 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Union[str, Any]      )     ->			Dict:
       UpperCAmelCase__							 =						RegNetModel(config=lowerCamelCase      )
       model.to(lowerCamelCase      )
       model.eval()
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       # 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 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :List[str]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.num_labels
       UpperCAmelCase__							 =						RegNetForImageClassification(lowerCamelCase      )
       model.to(lowerCamelCase      )
       model.eval()
       UpperCAmelCase__							 =						model(lowerCamelCase ,      labels=lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.num_labels)      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						config_and_inputs
       UpperCAmelCase__							 =						{"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      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 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       UpperCAmelCase__							 =						RegNetModelTester(self      )
       UpperCAmelCase__							 =						ConfigTester(self ,      config_class=lowerCamelCase ,      has_text_modality=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			Tuple:
       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 	UpperCAmelCase_						(			self   :Any      )     ->			List[str]:
       return
      @unittest.skip(reason="RegNet does not use inputs_embeds"      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Any:
       pass
      @unittest.skip(reason="RegNet does not support input and output embeddings"      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Union[str, Any]:
       pass
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        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] ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			int:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(config=lowerCamelCase      )
        for name, module in model.named_modules():
         if isinstance(lowerCamelCase ,      (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 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[Any]:
       def check_hidden_states_output(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :int ,      lowerCamelCase   :Optional[int]      ):
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        model.to(lowerCamelCase      )
        model.eval()

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

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

        UpperCAmelCase__							 =						self.model_tester.num_stages
        self.assertEqual(len(lowerCamelCase      ) ,      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] ,      )

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

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

         check_hidden_states_output(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase      )







      @slow
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							 =						RegNetModel.from_pretrained(lowerCamelCase      )
        self.assertIsNotNone(lowerCamelCase      )



def 						lowerCAmelCase		(		):




 """simple docstring"""







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


@require_torch
@require_vision
class 			_UpperCamelCase      (				unittest.TestCase		):
      @cached_property
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[Any]:
       return (
           AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
           if is_vision_available()
           else None
       )







      @slow
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						self.default_image_processor
       UpperCAmelCase__							 =						prepare_img()
       UpperCAmelCase__							 =						image_processor(images=lowerCamelCase ,      return_tensors="pt"      ).to(lowerCamelCase      )

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

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

       UpperCAmelCase__							 =						torch.tensor([-0.41_80, -1.50_51, -3.48_36]      ).to(lowerCamelCase      )

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





