



"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


snake_case   					=				logging.get_logger(__name__)



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	float							,       **__lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      feature_size
     _snake_case   	=      sampling_rate
     _snake_case   	=      padding_value

     _snake_case   	=      kwargs.pop('''padding_side'''							,       '''right'''				)
     _snake_case   	=      kwargs.pop('''return_attention_mask'''							,       __lowerCamelCase				)

     super().__init__(**__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Union[
           BatchFeature,
           List[BatchFeature],
           Dict[str, BatchFeature],
           Dict[str, List[BatchFeature]],
           List[Dict[str, BatchFeature]],
       ]							,       __lowerCamelCase    :	Union[bool, str, PaddingStrategy] = True							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[bool] = None							,       __lowerCamelCase    :	Optional[Union[str, TensorType]] = None							,       ):



     """simple docstring"""
     # If we have a list of dicts, let's convert it in a dict of lists
     # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
     if isinstance(__lowerCamelCase							,       (list, tuple)				) and isinstance(processed_features[0]							,       (dict, BatchFeature)				):
       _snake_case   	=      {
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

     # The model's main input name, usually `input_values`, has be passed for padding
     if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           f""" to this method that includes {self.model_input_names[0]}, but you provided"""
           f""" {list(processed_features.keys()				)}"""				)

     _snake_case   	=      processed_features[self.model_input_names[0]]
     _snake_case   	=      (
         return_attention_mask if return_attention_mask is not None else self.return_attention_mask
     )

     if len(__lowerCamelCase				) == 0:
       if return_attention_mask:
         _snake_case   	=      []
       return processed_features

     # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
     # and rebuild them afterwards if no return_tensors is specified
     # Note that we lose the specific device the tensor may be on for PyTorch

     _snake_case   	=      required_input[0]
     if isinstance(__lowerCamelCase							,       (list, tuple)				):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       _snake_case   	=      0
       while len(required_input[index]				) == 0:
         index += 1
       if index < len(__lowerCamelCase				):
         _snake_case   	=      required_input[index][0]

     if return_tensors is None:
       if is_tf_tensor(__lowerCamelCase				):
         _snake_case   	=      '''tf'''
       elif is_torch_tensor(__lowerCamelCase				):
         _snake_case   	=      '''pt'''
       elif isinstance(__lowerCamelCase							,       (int, float, list, tuple, np.ndarray)				):
         _snake_case   	=      '''np'''
       else:
         raise ValueError(
             f"""type of {first_element} unknown: {type(__lowerCamelCase				)}. """
             '''Should be one of a python, numpy, pytorch or tensorflow object.'''				)

     for key, value in processed_features.items():
       if isinstance(value[0]							,       (int, float)				):
         _snake_case   	=      to_numpy(__lowerCamelCase				)
       else:
         _snake_case   	=      [to_numpy(__lowerCamelCase				) for v in value]

        # Convert padding_strategy in PaddingStrategy
     _snake_case   	=      self._get_padding_strategies(padding=__lowerCamelCase							,       max_length=__lowerCamelCase				)

     _snake_case   	=      processed_features[self.model_input_names[0]]

     _snake_case   	=      len(__lowerCamelCase				)
     if not all(len(__lowerCamelCase				) == batch_size for v in processed_features.values()				):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''				)

     _snake_case   	=      []
     for i in range(__lowerCamelCase				):
       _snake_case   	=      {k: v[i] for k, v in processed_features.items()}
       # truncation
       _snake_case   	=      self._truncate(
           __lowerCamelCase							,       max_length=__lowerCamelCase							,       pad_to_multiple_of=__lowerCamelCase							,       truncation=__lowerCamelCase							,       )
       truncated_inputs.append(__lowerCamelCase				)

     if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       _snake_case   	=      max(len(input_slice[self.model_input_names[0]]				) for input_slice in truncated_inputs				)
       _snake_case   	=      PaddingStrategy.MAX_LENGTH

     _snake_case   	=      {}
     for i in range(__lowerCamelCase				):
       # padding
       _snake_case   	=      self._pad(
           truncated_inputs[i]							,       max_length=__lowerCamelCase							,       padding_strategy=__lowerCamelCase							,       pad_to_multiple_of=__lowerCamelCase							,       return_attention_mask=__lowerCamelCase							,       )

       for key, value in outputs.items():
         if key not in batch_outputs:
           _snake_case   	=      []
         if value.dtype is np.dtype(np.floataa				):
           _snake_case   	=      value.astype(np.floataa				)
         batch_outputs[key].append(__lowerCamelCase				)

     return BatchFeature(__lowerCamelCase							,       tensor_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Union[Dict[str, np.ndarray], BatchFeature]							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[bool] = None							,       ):



     """simple docstring"""
     _snake_case   	=      processed_features[self.model_input_names[0]]

     if padding_strategy == PaddingStrategy.LONGEST:
       _snake_case   	=      len(__lowerCamelCase				)

     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       _snake_case   	=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     _snake_case   	=      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCamelCase				) < max_length

     if return_attention_mask and "attention_mask" not in processed_features:
       _snake_case   	=      np.ones(len(__lowerCamelCase				)							,       dtype=np.intaa				)

     if needs_to_be_padded:
       _snake_case   	=      max_length - len(__lowerCamelCase				)
       if self.padding_side == "right":
         if return_attention_mask:
           _snake_case   	=      np.pad(
               processed_features['''attention_mask''']							,       (0, difference)				)
         _snake_case   	=      ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
         _snake_case   	=      np.pad(
             __lowerCamelCase							,       __lowerCamelCase							,       '''constant'''							,       constant_values=self.padding_value				)
       elif self.padding_side == "left":
         if return_attention_mask:
           _snake_case   	=      np.pad(
               processed_features['''attention_mask''']							,       (difference, 0)				)
         _snake_case   	=      ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
         _snake_case   	=      np.pad(
             __lowerCamelCase							,       __lowerCamelCase							,       '''constant'''							,       constant_values=self.padding_value				)
       else:
         raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

     return processed_features



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	Union[Dict[str, np.ndarray], BatchFeature]							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[bool] = None							,       ):



     """simple docstring"""
     if not truncation:
       return processed_features
     elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''				)

     _snake_case   	=      processed_features[self.model_input_names[0]]

     # find `max_length` that fits `pad_to_multiple_of`
     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       _snake_case   	=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     _snake_case   	=      len(__lowerCamelCase				) > max_length

     if needs_to_be_truncated:
       _snake_case   	=      processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
         _snake_case   	=      processed_features['''attention_mask'''][:max_length]

     return processed_features





   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	str=False							,       __lowerCamelCase    :	Any=None				):



     """simple docstring"""

     # Get padding strategy
     if padding is not False:
       if padding is True:
         _snake_case   	=      PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         _snake_case   	=      PaddingStrategy(__lowerCamelCase				)
       elif isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         _snake_case   	=      padding
     else:
       _snake_case   	=      PaddingStrategy.DO_NOT_PAD

     # Set max length if needed
     if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
         raise ValueError(
             f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""				)

        # Test if we have a padding value
     if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''				)

     return padding_strategy










"""simple docstring"""






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

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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


snake_case   					=				get_tests_dir('''fixtures/dummy-config.json''')



class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      0



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     self.assertIsNotNone(transformers.models.auto.__spec__				)
     self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto'''				)				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      AutoConfig.from_pretrained('''bert-base-uncased'''				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      AutoConfig.for_model('''roberta'''				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     with tempfile.TemporaryDirectory() as tmp_dir:
       # This model name contains bert and roberta, but roberta ends up being picked.
       _snake_case   	=      os.path.join(__lowerCamelCase							,       '''fake-roberta'''				)
       os.makedirs(__lowerCamelCase							,       exist_ok=__lowerCamelCase				)
       with open(os.path.join(__lowerCamelCase							,       '''config.json'''				)							,       '''w'''				) as f:
         f.write(json.dumps({}				)				)
       _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase				)
       self.assertEqual(type(__lowerCamelCase				)							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     try:
       AutoConfig.register('''custom'''							,       __lowerCamelCase				)
       # Wrong model type will raise an error
       with self.assertRaises(__lowerCamelCase				):
         AutoConfig.register('''model'''							,       __lowerCamelCase				)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(__lowerCamelCase				):
         AutoConfig.register('''bert'''							,       __lowerCamelCase				)

       # Now that the config is registered, it can be used as any other config with the auto-API
       _snake_case   	=      CustomConfig()
       with tempfile.TemporaryDirectory() as tmp_dir:
         config.save_pretrained(__lowerCamelCase				)
         _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase				)
         self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)

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



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''bert-base is not a local folder and is not a valid model identifier'''				):
       _snake_case   	=      AutoConfig.from_pretrained('''bert-base'''				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
       _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase							,       revision='''aaaaaa'''				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'''							,       ):
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo'''				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(__lowerCamelCase				):
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''				)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(__lowerCamelCase				):
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,       trust_remote_code=__lowerCamelCase				)

     _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,       trust_remote_code=__lowerCamelCase				)
     self.assertEqual(config.__class__.__name__							,       '''NewModelConfig'''				)

     # Test config can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(__lowerCamelCase				)
       _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase							,       trust_remote_code=__lowerCamelCase				)
     self.assertEqual(reloaded_config.__class__.__name__							,       '''NewModelConfig'''				)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""



     class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
        A__		:	List[Any]           =  '''new-model'''

     try:
       AutoConfig.register('''new-model'''							,       __lowerCamelCase				)
       # If remote code is not set, the default is to use local
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''				)
       self.assertEqual(config.__class__.__name__							,       '''NewModelConfigLocal'''				)

       # If remote code is disabled, we load the local one.
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,       trust_remote_code=__lowerCamelCase				)
       self.assertEqual(config.__class__.__name__							,       '''NewModelConfigLocal'''				)

       # If remote is enabled, we load from the Hub
       _snake_case   	=      AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,       trust_remote_code=__lowerCamelCase				)
       self.assertEqual(config.__class__.__name__							,       '''NewModelConfig'''				)

     finally:
       if "new-model" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["new-model"]





