"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





	return ConvertCommand(
	    args.model_type      ,							args.tf_checkpoint      ,							args.pytorch_dump_output      ,							args.config      ,							args.finetuning_task_name  )


_a					:					str=			"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			parser.add_parser(
					    'convert'     ,  help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'     ,  )
					train_parser.add_argument('--model_type'     ,  type=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					if self._model_type == "albert":
						try:
							from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "bert":
						try:
							from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "funnel":
						try:
							from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "t5":
						try:
							from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "transfo_xl":
						try:
							from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
							    convert_transfo_xl_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					elif self._model_type == "gpt2":
						try:
							from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
							    convert_gpta_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "xlnet":
						try:
							from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
							    convert_xlnet_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output     ,  self._finetuning_task_name)
					elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					else:
						raise ValueError(
						    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')






"""simple docstring"""







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			0



				def  _lowercase					(self    :				Tuple)   ->     int:
					__snake_case      :							Optional[Any]						=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							List[str]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							Any						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							List[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							str						=			CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						__snake_case      :							List[Any]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case      :							Optional[int]						=			CLIPImageProcessor(**_A)

						# save in new folder
						model_config.save_pretrained(_A)
						config.save_pretrained(_A)

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)

						# make sure private variable is not incorrectly saved
						__snake_case      :							int						=			json.loads(config.to_json_string())
						self.assertTrue('_processor_class' not in dict_as_saved)

					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							int						=			Path(_A) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )

						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					with self.assertRaisesRegex(
					    _A     ,  'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('clip-base')



				def  _lowercase					(self    :				str)   ->     int:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							str						=			AutoImageProcessor.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				List[Any])   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,  ):
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_A):
						__snake_case      :							Any						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_A):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)

					__snake_case      :							Union[str, Any]						=			AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
					self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(_A)
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A     ,  trust_remote_code=_A)
					self.assertEqual(reloaded_image_processor.__class__.__name__     ,  'NewImageProcessor')



				def  _lowercase					(self    :				int)   ->     Optional[int]:
					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(_A):
							AutoImageProcessor.register(_A     ,  _A)

						with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case      :							Tuple						=			Path(_A) / 'preprocessor_config.json'
							__snake_case      :							Dict						=			Path(_A) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
							json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

							__snake_case      :							Tuple						=			CustomImageProcessor.from_pretrained(_A)

						# Now that the config is registered, it can be used as any other config with the auto-API
						with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_A)
							__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
							self.assertIsInstance(_A     ,  _A)

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






				def  _lowercase					(self    :				List[Any])   ->     Tuple:

					class 		UpperCamelCase      (   lowercase						):
									UpperCAmelCase    :				str   =		True

					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# If remote code is not set, the default is to use local
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote code is disabled, we load the local one.
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote is enabled, we load from the Hub
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(not hasattr(_A     ,  'is_local'))

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





