

'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					"""mobilenet_v1"""



 def __init__(   self		:   List[Any]			,				a__		:   Tuple=3			,				a__		:   Union[str, Any]=224			,				a__		:   int=1.0			,				a__		:   Tuple=8			,				a__		:   Optional[int]="relu6"			,				a__		:   Any=True			,				a__		:   str=0.999			,				a__		:   Optional[int]=0.02			,				a__		:   Optional[int]=0.001			,				**a__		:   int			,				):
      super().__init__(**a__      )

      if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''      )

      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    depth_multiplier
      __magic_name__						       =    min_depth
      __magic_name__						       =    hidden_act
      __magic_name__						       =    tf_padding
      __magic_name__						       =    classifier_dropout_prob
      __magic_name__						       =    initializer_range
      __magic_name__						       =    layer_norm_eps






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					version.parse("""1.11"""     )



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      return OrderedDict([('''pixel_values''', {0: '''batch'''})]      )



 @property
 def 			snake_case__  (   self		:   Any      ):
      if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]      )
      else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]      )



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return 1E-4




'''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


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

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

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

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

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

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

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

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

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



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

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

      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 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

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

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






