



'''simple docstring'''
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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						# Construct model
						if gpta_config_file == "":
												_UpperCamelCase   :						Any												=  GPTaConfig()
						else:
												_UpperCamelCase   :						Optional[int]												=  GPTaConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  GPTaModel(UpperCAmelCase_				)

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

						# Save pytorch-model
						_UpperCamelCase   :						List[str]												=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
						_UpperCamelCase   :						Tuple												=  pytorch_dump_folder_path + '/' + CONFIG_NAME
						print(f'Save PyTorch model to {pytorch_weights_dump_path}'				)
						torch.save(model.state_dict()    ,    UpperCAmelCase_				)
						print(f'Save configuration file to {pytorch_config_dump_path}'				)
						with open(UpperCAmelCase_    ,    'w'    ,    encoding='utf-8'				) as f:
												f.write(config.to_json_string()				)


if __name__ == "__main__":
					snake_case_					:      Optional[int]    			=    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.'
					    ),
					)
					snake_case_					:      Optional[int]    			=    parser.parse_args()
					convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf

if is_vision_available():
					from PIL import Image






def A__       (	UpperCAmelCase_=""				):
						_UpperCamelCase   :						Any												=  tempfile.mkdtemp()
						return os.path.join(UpperCAmelCase_    ,    str(uuid.uuida()				) + suffix				)






@require_soundfile
@require_torch
class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Optional[int]												=  AgentAudio(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)

							del agent_type

							# Ensure the path remains even after the object deletion
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)

							# Ensure that the file contains the same value as the original tensor
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  sf.read(lowerCamelCase__					)
							self.assertTrue(torch.allclose(lowerCamelCase__					,torch.tensor(lowerCamelCase__					)					,atol=1E-4					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Any												=  get_new_path(suffix='.wav'					)
							sf.write(lowerCamelCase__					,lowerCamelCase__					,16000					)

							_UpperCamelCase   :						List[Any]												=  AgentAudio(lowerCamelCase__					)

							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)
							self.assertEqual(agent_type.to_string()					,lowerCamelCase__					)






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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  torch.randint(0					,256					,(64, 64, 3)					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type._tensor					,atol=1E-4					)					)

							self.assertIsInstance(agent_type.to_raw()					,Image.Image					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Tuple												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertTrue(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Union[str, Any]												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertFalse(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)






class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  'Hey!'
							_UpperCamelCase   :						Optional[int]												=  AgentText(lowerCamelCase__					)

							self.assertEqual(lowerCamelCase__					,agent_type.to_string()					)
							self.assertEqual(lowerCamelCase__					,agent_type.to_raw()					)
							self.assertEqual(lowerCamelCase__					,lowerCamelCase__					)


