

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


lowerCAmelCase_       	=	get_tests_dir('''fixtures''')





class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									# A mock response for an HTTP head request to emulate server down
									snake_case_   =	mock.Mock()
									snake_case_   =	5_0_0
									snake_case_   =	{}
									snake_case_   =	HTTPError
									snake_case_   =	{}

									# Download this model to make sure it's in the cache.
									snake_case_   =	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=_UpperCamelCase							) as mock_head:
															snake_case_   =	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''							)
															# This check we did call the fake head request
															mock_head.assert_called()







			def 				snake_case__(     self :				List[str]							)      ->Optional[int]:
									# This test is for deprecated behavior and can be removed in v5
									snake_case_   =	ViTImageProcessor.from_pretrained(
									    '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''							)







			def 				snake_case__(     self :				Any							)      ->List[str]:
									with self.assertRaises(_UpperCamelCase							):
															# config is in subfolder, the following should not work without specifying the subfolder
															snake_case_   =	AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''							)

									snake_case_   =	AutoImageProcessor.from_pretrained(
									    '''hf-internal-testing/stable-diffusion-all-variants'''							,   subfolder='''feature_extractor'''							)

									self.assertIsNotNone(_UpperCamelCase							)






@is_staging_test
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@classmethod
			def 				snake_case__(     cls :				List[Any]							)      ->Dict:
									snake_case_   =	TOKEN
									HfFolder.save_token(_UpperCamelCase							)







			@classmethod
			def 				snake_case__(     cls :				List[Any]							)      ->Optional[int]:
									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 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	ViTImageProcessor.from_pretrained(_UpperCamelCase							)
									image_processor.push_to_hub('''test-image-processor'''							,   use_auth_token=self._token							)

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

									# 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(
															    _UpperCamelCase							,   repo_id='''test-image-processor'''							,   push_to_hub=_UpperCamelCase							,   use_auth_token=self._token							)

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







			def 				snake_case__(     self :				str							)      ->List[str]:
									snake_case_   =	ViTImageProcessor.from_pretrained(_UpperCamelCase							)
									image_processor.push_to_hub('''valid_org/test-image-processor'''							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)

									# 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(
															    _UpperCamelCase							,   repo_id='''valid_org/test-image-processor-org'''							,   push_to_hub=_UpperCamelCase							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)







			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									CustomImageProcessor.register_for_auto_class()
									snake_case_   =	CustomImageProcessor.from_pretrained(_UpperCamelCase							)

									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'''}							,   )

									snake_case_   =	AutoImageProcessor.from_pretrained(
									    f'''{USER}/test-dynamic-image-processor'''							,   trust_remote_code=_UpperCamelCase							)
									# 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'''							)
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       A	(    _lowercase						):
						if "model" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.replace('''model.'''		,  ''''''						)
						if "norm1" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''norm2'''		,  '''output.LayerNorm'''						)
						if "norm" in orig_key:
												SCREAMING_SNAKE_CASE							:    Tuple							=							orig_key.replace('''norm'''		,  '''LayerNorm'''						)
						if "transformer" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												SCREAMING_SNAKE_CASE							:    List[str]							=							orig_key.replace(f"""transformer_{layer_num}"""		,  f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''mha.attn'''		,  '''attention.self'''						)
						if "mha" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mha'''		,  '''attention'''						)
						if "W_q" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''W_q'''		,  '''self.query'''						)
						if "W_k" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''W_k'''		,  '''self.key'''						)
						if "W_v" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''W_v'''		,  '''self.value'''						)
						if "ff1" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''ff1'''		,  '''intermediate.dense'''						)
						if "ff2" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''ff2'''		,  '''output.dense'''						)
						if "ff" in orig_key:
												SCREAMING_SNAKE_CASE							:    Dict							=							orig_key.replace('''ff'''		,  '''output.dense'''						)
						if "mlm_class" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''						)
						if "cls" not in orig_key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''yoso.''' + orig_key

						return orig_key
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							orig_state_dict.pop(_lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val

						SCREAMING_SNAKE_CASE							:    List[str]							=							orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.arange(_lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model_state_dict''']
						SCREAMING_SNAKE_CASE							:    List[Any]							=							YosoConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							YosoForMaskedLM(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_checkpoint_helper(config.max_position_embeddings		,  _lowercase						)

						print(model.load_state_dict(_lowercase						)						)
						model.eval()
						model.save_pretrained(_lowercase						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					__UpperCamelCase		:       Tuple									=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					__UpperCamelCase		:       Optional[Any]									=     parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





