

'''simple docstring'''

import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	str,       lowercase__    :	List[str]			):






     '''simple docstring'''





     __lowercase      		=AlbertConfig.from_json_file(lowercase__			)
     print(F'''Building PyTorch model from configuration: {config}'''			)
     __lowercase      		=AlbertForPreTraining(lowercase__			)

     # Load weights from tf checkpoint
     load_tf_weights_in_albert(lowercase__,       lowercase__,       lowercase__			)

     # Save pytorch-model
     print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
     torch.save(model.state_dict(),       lowercase__			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--albert_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained ALBERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       UpperCAmelCase					 =	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_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 AutoFeatureExtractor, WavaVecaFeatureExtractor
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_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


