


import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							:						str							     =   logging.get_logger(__name__)

lowerCamelCase__							:						int							     =   {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       'data2vec-audio'

     def __init__(					self:Optional[int]     ,		_a:str=32     ,		_a:Tuple=7_68     ,		_a:List[str]=12     ,		_a:int=12     ,		_a:List[str]=30_72     ,		_a:int="gelu"     ,		_a:Dict=0.1     ,		_a:int=0.1     ,		_a:int=0.1     ,		_a:int=0.0     ,		_a:Optional[Any]=0.1     ,		_a:List[Any]=0.1     ,		_a:Union[str, Any]=0.02     ,		_a:List[str]=1e-5     ,		_a:Any="gelu"     ,		_a:Tuple=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     ,		_a:Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)     ,		_a:List[Any]=(10, 3, 3, 3, 3, 2, 2)     ,		_a:Any=False     ,		_a:Dict=16     ,		_a:Dict=19     ,		_a:Optional[int]=5     ,		_a:Union[str, Any]=0.05     ,		_a:Any=10     ,		_a:Tuple=2     ,		_a:Tuple=0.0     ,		_a:List[str]=10     ,		_a:int=0     ,		_a:Tuple="sum"     ,		_a:Any=False     ,		_a:List[Any]=False     ,		_a:str=2_56     ,		_a:Dict=(5_12, 5_12, 5_12, 5_12, 15_00)     ,		_a:str=(5, 3, 3, 1, 1)     ,		_a:List[str]=(1, 2, 3, 1, 1)     ,		_a:List[Any]=5_12     ,		_a:Union[str, Any]=0     ,		_a:Optional[Any]=1     ,		_a:Tuple=2     ,		_a:int=False     ,		_a:Any=3     ,		_a:int=2     ,		_a:Optional[Any]=3     ,		_a:Any=None     ,		**_a:Optional[Any]     ,		):
       super().__init__(**_a     ,		pad_token_id=_a     ,		bos_token_id=_a     ,		eos_token_id=_a   )
       snake_case__						= hidden_size
       snake_case__						= feat_extract_activation
       snake_case__						= list(_a   )
       snake_case__						= list(_a   )
       snake_case__						= list(_a   )
       snake_case__						= conv_bias
       snake_case__						= num_conv_pos_embeddings
       snake_case__						= num_conv_pos_embedding_groups
       snake_case__						= conv_pos_kernel_size
       snake_case__						= len(self.conv_dim   )
       snake_case__						= num_hidden_layers
       snake_case__						= intermediate_size
       snake_case__						= hidden_act
       snake_case__						= num_attention_heads
       snake_case__						= hidden_dropout
       snake_case__						= attention_dropout
       snake_case__						= activation_dropout
       snake_case__						= feat_proj_dropout
       snake_case__						= final_dropout
       snake_case__						= layerdrop
       snake_case__						= layer_norm_eps
       snake_case__						= initializer_range
       snake_case__						= vocab_size
       snake_case__						= use_weighted_layer_sum

       if (
           (len(self.conv_stride   ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
           or (len(self.conv_dim   ) != self.num_feat_extract_layers)
       ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             F""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
             F""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       snake_case__						= mask_time_prob
       snake_case__						= mask_time_length
       snake_case__						= mask_time_min_masks
       snake_case__						= mask_feature_prob
       snake_case__						= mask_feature_length
       snake_case__						= mask_feature_min_masks

       # ctc loss
       snake_case__						= ctc_loss_reduction
       snake_case__						= ctc_zero_infinity

       # adapter
       snake_case__						= add_adapter
       snake_case__						= adapter_kernel_size
       snake_case__						= adapter_stride
       snake_case__						= num_adapter_layers
       snake_case__						= output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       snake_case__						= classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       snake_case__						= list(_a   )
       snake_case__						= list(_a   )
       snake_case__						= list(_a   )
       snake_case__						= xvector_output_dim







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       return math.prod(self.conv_stride   )





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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= 0

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= 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__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= 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__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= 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__						= AutoImageProcessor.from_pretrained(_a   ).to_dict()

         config_dict.pop('''image_processor_type'''   )
         snake_case__						= CLIPImageProcessor(**_a   )

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

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )

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

       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       with self.assertRaisesRegex(
           _a     ,		'''clip-base is not a local folder and is not a valid model identifier'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained('''clip-base'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       with self.assertRaisesRegex(
           _a     ,		r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained(_a     ,		revision='''aaaaaa'''   )

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

     def 	SCREAMING_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   ):
         snake_case__						= 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__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )

       snake_case__						= 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__						= AutoImageProcessor.from_pretrained(_a     ,		trust_remote_code=_a   )
       self.assertEqual(reloaded_image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       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__						= Path(_a   ) / '''preprocessor_config.json'''
           snake_case__						= 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__						= 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__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       class 		__magic_name__						(snake_case_				):





            '''simple docstring'''



            __lowercase				:							List[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__						= 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__						= 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__						= 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]

