



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def     a(      A       :    List[str] ,     A       :    str ,     A       :    List[Any] ,     A       :    Dict       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   s.rsplit(_lowercase ,     _lowercase       )
					return new.join(_lowercase       )






def     a(      A       :    List[str]       )		->       str:
					"""simple docstring"""





					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()       )






def     a(      A       :    int       )		->       Tuple:
					"""simple docstring"""





					a					     =   {}

					a					     =   ["""group_1""", """group_2""", """group_3""", """group_4"""]
					for key, value in state_dict.items():
										for group_key in group_keys:
															if group_key in key:
																				a					     =   key.replace(f'''{group_key}.''' ,     f'''{group_key}.group.'''       )

										if "res_path" in key:
															a					     =   key.replace("res_path." ,     "res_path.path."       )

										if key.endswith(".w"       ):
															a					     =   rreplace(_lowercase ,     ".w" ,     ".weight" ,     1       )
										if key.endswith(".b"       ):
															a					     =   rreplace(_lowercase ,     ".b" ,     ".bias" ,     1       )

										a					     =   value.float()

					return upgrade






@torch.no_grad()
def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    int=None ,     A       :    str=True       )		->       Union[str, Any]:
					"""simple docstring"""





					from dall_e import Encoder

					a					     =   Encoder()
					if os.path.exists(_lowercase       ):
										a					     =   torch.load(_lowercase       )
					else:
										a					     =   torch.hub.load_state_dict_from_url(_lowercase       )

					if isinstance(_lowercase ,     _lowercase       ):
										a					     =   ckpt.state_dict()
					encoder.load_state_dict(_lowercase       )

					if config_path is not None:
										a					     =   FlavaImageCodebookConfig.from_pretrained(_lowercase       )
					else:
										a					     =   FlavaImageCodebookConfig()

					a					     =   FlavaImageCodebook(_lowercase       ).eval()
					a					     =   encoder.state_dict()

					a					     =   upgrade_state_dict(_lowercase       )
					hf_model.load_state_dict(_lowercase       )
					a					     =   hf_model.state_dict()
					a					     =   count_parameters(_lowercase       )
					a					     =   count_parameters(_lowercase       )

					assert torch.allclose(_lowercase ,     _lowercase ,     atol=1e-3       )

					if save_checkpoint:
										hf_model.save_pretrained(_lowercase       )
					else:
										return hf_state_dict


if __name__ == "__main__":
				_lowercase: Optional[int]      			=		argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				_lowercase: Optional[int]      			=		parser.parse_args()

				convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


a   :			List[str]					  =    get_tests_dir("""fixtures""")

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			int:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase     :		Tuple							     =		mock.Mock()
								UpperCAmelCase     :		List[str]							     =		500
								UpperCAmelCase     :		Any							     =		{}
								UpperCAmelCase     :		List[str]							     =		HTTPError
								UpperCAmelCase     :		str							     =		{}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch("""requests.Session.request"""		,		return_value=A    ) as mock_head:
															UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
															# This check we did call the fake head request
															mock_head.assert_called()





	def  _lowercase(  self    )			->			Any:
								# This test is for deprecated behavior and can be removed in v5
								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(
								    """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""    )






	def  _lowercase(  self    )			->			Union[str, Any]:
								with self.assertRaises(A    ):
															# config is in subfolder, the following should not work without specifying the subfolder
															UpperCAmelCase     :		Any							     =		AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    """hf-internal-testing/stable-diffusion-all-variants"""		,		subfolder="""feature_extractor"""    )

								self.assertIsNotNone(A    )




@is_staging_test
class 			UpperCamelCase_ (							unittest.TestCase			):





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		TOKEN
								HfFolder.save_token(A    )





	@classmethod
	def  _lowercase(  cls    )			->			List[str]:
								try:
															delete_repo(token=cls._token		,		repo_id="""test-image-processor"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""valid_org/test-image-processor-org"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""test-dynamic-image-processor"""    )
								except HTTPError:
															pass





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Union[str, Any]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""test-image-processor"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""valid_org/test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""valid_org/test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""valid_org/test-image-processor-org"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		int							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )






	def  _lowercase(  self    )			->			Optional[int]:
								CustomImageProcessor.register_for_auto_class()
								UpperCAmelCase     :		Optional[Any]							     =		CustomImageProcessor.from_pretrained(A    )

								image_processor.push_to_hub("""test-dynamic-image-processor"""		,		use_auth_token=self._token    )

								# This has added the proper auto_map field to the config
								self.assertDictEqual(
								    image_processor.auto_map		,		{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    f'''{USER}/test-dynamic-image-processor'''		,		trust_remote_code=A    )
								# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
								self.assertEqual(new_image_processor.__class__.__name__		,		"""CustomImageProcessor"""    )

