



"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor

@require_vision
class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  Union[str, Any]     )->					Dict:
								lowerCamelCase__		:		int			=tempfile.mkdtemp()

								# fmt: off
								lowerCamelCase__		:		Tuple			=['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
								# fmt: on
								lowerCamelCase__		:		Any			=dict(zip(lowerCamelCase,  range(len(lowerCamelCase     )     )     )     )
								lowerCamelCase__		:		str			=['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
								lowerCamelCase__		:		Optional[Any]			={'''unk_token''': '''<unk>'''}

								lowerCamelCase__		:		Tuple			=os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']     )
								lowerCamelCase__		:		Dict			=os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']     )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''     ) as fp:
											fp.write(json.dumps(lowerCamelCase     ) + '''\n'''     )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''     ) as fp:
											fp.write('''\n'''.join(lowerCamelCase     )     )

								lowerCamelCase__		:		List[Any]			={
								    '''do_resize''': True,
								    '''size''': 20,
								    '''do_center_crop''': True,
								    '''crop_size''': 18,
								    '''do_normalize''': True,
								    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								lowerCamelCase__		:		Any			=os.path.join(self.tmpdirname,  lowerCamelCase     )
								with open(self.image_processor_file,  '''w''',  encoding='''utf-8'''     ) as fp:
											json.dump(lowerCamelCase,  lowerCamelCase     )



					def 		snake_case       (       self							:  Any,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return CLIPTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase     )



					def 		snake_case       (       self							:  List[str],  **lowerCamelCase							:  Optional[int]     )->					List[Any]:
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname,  **lowerCamelCase     )



					def 		snake_case       (       self							:  str,  **lowerCamelCase							:  List[str]     )->					List[str]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Optional[Any]:
								shutil.rmtree(self.tmpdirname     )



					def 		snake_case       (       self							:  Optional[int]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta     )]

								lowerCamelCase__		:		Optional[int]			=[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1     )     ) for x in image_inputs]

								return image_inputs



					def 		snake_case       (       self							:  Any     )->					int:
								lowerCamelCase__		:		int			=self.get_tokenizer()
								lowerCamelCase__		:		Any			=self.get_rust_tokenizer()
								lowerCamelCase__		:		Dict			=self.get_image_processor()

								lowerCamelCase__		:		List[Any]			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )
								processor_slow.save_pretrained(self.tmpdirname     )
								lowerCamelCase__		:		Optional[int]			=CLIPSegProcessor.from_pretrained(self.tmpdirname,  use_fast=lowerCamelCase     )

								lowerCamelCase__		:		Union[str, Any]			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )
								processor_fast.save_pretrained(self.tmpdirname     )
								lowerCamelCase__		:		List[str]			=CLIPSegProcessor.from_pretrained(self.tmpdirname     )

								self.assertEqual(processor_slow.tokenizer.get_vocab(),  tokenizer_slow.get_vocab()     )
								self.assertEqual(processor_fast.tokenizer.get_vocab(),  tokenizer_fast.get_vocab()     )
								self.assertEqual(tokenizer_slow.get_vocab(),  tokenizer_fast.get_vocab()     )
								self.assertIsInstance(processor_slow.tokenizer,  lowerCamelCase     )
								self.assertIsInstance(processor_fast.tokenizer,  lowerCamelCase     )

								self.assertEqual(processor_slow.image_processor.to_json_string(),  image_processor.to_json_string()     )
								self.assertEqual(processor_fast.image_processor.to_json_string(),  image_processor.to_json_string()     )
								self.assertIsInstance(processor_slow.image_processor,  lowerCamelCase     )
								self.assertIsInstance(processor_fast.image_processor,  lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Any:
								lowerCamelCase__		:		int			=CLIPSegProcessor(tokenizer=self.get_tokenizer(),  image_processor=self.get_image_processor()     )
								processor.save_pretrained(self.tmpdirname     )

								lowerCamelCase__		:		Union[str, Any]			=self.get_tokenizer(bos_token='''(BOS)''',  eos_token='''(EOS)'''     )
								lowerCamelCase__		:		Any			=self.get_image_processor(do_normalize=lowerCamelCase,  padding_value=1.0     )

								lowerCamelCase__		:		Union[str, Any]			=CLIPSegProcessor.from_pretrained(
								    self.tmpdirname,  bos_token='''(BOS)''',  eos_token='''(EOS)''',  do_normalize=lowerCamelCase,  padding_value=1.0     )

								self.assertEqual(processor.tokenizer.get_vocab(),  tokenizer_add_kwargs.get_vocab()     )
								self.assertIsInstance(processor.tokenizer,  lowerCamelCase     )

								self.assertEqual(processor.image_processor.to_json_string(),  image_processor_add_kwargs.to_json_string()     )
								self.assertIsInstance(processor.image_processor,  lowerCamelCase     )



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=self.get_image_processor()
								lowerCamelCase__		:		List[Any]			=self.get_tokenizer()

								lowerCamelCase__		:		int			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			=self.prepare_image_inputs()

								lowerCamelCase__		:		Dict			=image_processor(lowerCamelCase,  return_tensors='''np'''     )
								lowerCamelCase__		:		Tuple			=processor(images=lowerCamelCase,  return_tensors='''np'''     )

								for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum(),  input_processor[key].sum(),  delta=1E-2     )



					def 		snake_case       (       self							:  str     )->					int:
								lowerCamelCase__		:		int			=self.get_image_processor()
								lowerCamelCase__		:		Optional[int]			=self.get_tokenizer()

								lowerCamelCase__		:		Optional[int]			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Any			='''lower newer'''

								lowerCamelCase__		:		str			=processor(text=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=tokenizer(lowerCamelCase     )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key],  encoded_processor[key]     )



					def 		snake_case       (       self							:  Dict     )->					Dict:
								lowerCamelCase__		:		Any			=self.get_image_processor()
								lowerCamelCase__		:		List[Any]			=self.get_tokenizer()

								lowerCamelCase__		:		Tuple			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Any			='''lower newer'''
								lowerCamelCase__		:		int			=self.prepare_image_inputs()

								lowerCamelCase__		:		Any			=processor(text=lowerCamelCase,  images=lowerCamelCase     )

								self.assertListEqual(list(inputs.keys()     ),  ['''input_ids''', '''attention_mask''', '''pixel_values''']     )

								# test if it raises when no input is passed
								with pytest.raises(lowerCamelCase     ):
											processor()



					def 		snake_case       (       self							:  Any     )->					List[str]:
								lowerCamelCase__		:		Dict			=self.get_image_processor()
								lowerCamelCase__		:		Tuple			=self.get_tokenizer()

								lowerCamelCase__		:		Any			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=self.prepare_image_inputs()
								lowerCamelCase__		:		Optional[Any]			=self.prepare_image_inputs()

								lowerCamelCase__		:		List[str]			=processor(images=lowerCamelCase,  visual_prompt=lowerCamelCase     )

								self.assertListEqual(list(inputs.keys()     ),  ['''pixel_values''', '''conditional_pixel_values''']     )

								# test if it raises when no input is passed
								with pytest.raises(lowerCamelCase     ):
											processor()




					def 		snake_case       (       self							:  Union[str, Any]     )->					Any:
								lowerCamelCase__		:		Union[str, Any]			=self.get_image_processor()
								lowerCamelCase__		:		Tuple			=self.get_tokenizer()

								lowerCamelCase__		:		List[str]			=CLIPSegProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__		:		Union[str, Any]			=processor.batch_decode(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=tokenizer.batch_decode(lowerCamelCase     )

								self.assertListEqual(lowerCamelCase,  lowerCamelCase     )









"""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      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

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

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=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__":
		_lowercase : Optional[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."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




