




'''simple docstring'''


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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_snake_case				:				Dict       							=		get_tests_dir('fixtures')
_snake_case				:				List[str]       							=		get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case				:				Tuple       							=		get_tests_dir('fixtures/dummy-config.json')

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  str )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    0







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Any:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
														self.assertIsInstance(lowercase_							,      lowercase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_ )
														self.assertIsInstance(lowercase_							,      lowercase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							int:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a     					=    WavaVecaConfig()

																					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_ ).to_dict()

																					config_dict.pop('''feature_extractor_type''' )
																					_a     					=    WavaVecaFeatureExtractor(**lowercase_ )

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

																					_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_ )

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

														self.assertIsInstance(lowercase_							,      lowercase_ )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Tuple:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_ )
														self.assertIsInstance(lowercase_							,      lowercase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowercase_							,      '''bert-base is not a local folder and is not a valid model identifier''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''bert-base''' )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowercase_							,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_							,      revision='''aaaaaa''' )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Union[str, Any]:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowercase_							,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,      ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							int:


														"""simple docstring"""


														with self.assertRaises(lowercase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
														# If remote code is disabled, we can't load this config.
														with self.assertRaises(lowercase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowercase_ )

														_a     					=    AutoFeatureExtractor.from_pretrained(
														    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowercase_ )
														self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )

														# Test feature extractor can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																					feature_extractor.save_pretrained(lowercase_ )
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_							,      trust_remote_code=lowercase_ )
														self.assertEqual(reloaded_feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )







							def        __lowerCAmelCase      (  self	:  int )      ->							List[Any]:


														"""simple docstring"""


														try:
																					AutoConfig.register('''custom'''							,      lowercase_ )
																					AutoFeatureExtractor.register(lowercase_							,      lowercase_ )
																					# Trying to register something existing in the Transformers library will raise an error
																					with self.assertRaises(lowercase_ ):
																												AutoFeatureExtractor.register(lowercase_							,      lowercase_ )

																					# Now that the config is registered, it can be used as any other config with the auto-API
																					_a     					=    CustomFeatureExtractor.from_pretrained(lowercase_ )
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												feature_extractor.save_pretrained(lowercase_ )
																												_a     					=    AutoFeatureExtractor.from_pretrained(lowercase_ )
																												self.assertIsInstance(lowercase_							,      lowercase_ )

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







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														class       A   (		a_   ):
																					lowercase_          =    True

														try:
																					AutoConfig.register('''custom'''							,      lowercase_ )
																					AutoFeatureExtractor.register(lowercase_							,      lowercase_ )
																					# If remote code is not set, the default is to use local
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote code is disabled, we load the local one.
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowercase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote is enabled, we load from the Hub
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowercase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(not hasattr(lowercase_							,      '''is_local''' ) )

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







'''simple docstring'''


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 		snake_case_   (UpperCamelCase	:  Namespace				):





							'''simple docstring'''


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


_snake_case				:				List[Any]       							=		'\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       A   (		_a   ):







							@staticmethod
							def        __lowerCAmelCase      (  lowerCAmelCase_	:  ArgumentParser )      ->							Any:


														"""simple docstring"""


														_a     					=    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=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Model\'s type.''' )
														train_parser.add_argument(
														    '''--tf_checkpoint'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''TensorFlow checkpoint path or folder.''' )
														train_parser.add_argument(
														    '''--pytorch_dump_output'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Path to the PyTorch saved model output.''' )
														train_parser.add_argument('''--config'''							,      type=lowerCAmelCase_							,      default=''''''							,      help='''Configuration file path or folder.''' )
														train_parser.add_argument(
														    '''--finetuning_task_name'''							,      type=lowerCAmelCase_							,      default=lowerCAmelCase_							,      help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,      )
														train_parser.set_defaults(func=lowerCAmelCase_ )







							def __init__(  self	:  int							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  Optional[Any]							,      )      ->							List[str]:


														"""simple docstring"""


														_a     					=    logging.get_logger('''transformers-cli/converting''' )

														self._logger.info(F'Loading model {model_type}' )
														_a     					=    model_type
														_a     					=    tf_checkpoint
														_a     					=    pytorch_dump_output
														_a     					=    config
														_a     					=    finetuning_task_name







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""simple docstring"""


														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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					if "ckpt" in self._tf_checkpoint.lower():
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					else:
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					convert_transfo_xl_checkpoint_to_pytorch(
																					    lowerCAmelCase_							,      self._config							,      self._pytorch_dump_output							,      lowerCAmelCase_ )
														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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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]''' )

