





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__UpperCAmelCase							=   logging.get_logger(__name__)

__UpperCAmelCase							=   OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__UpperCAmelCase							=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def       __UpperCamelCase					(      lowercase__						:							List[str]		)						->   List[Any]:
    '''simple docstring'''





    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowerCAmelCase_       :						str							= model_type_to_module_name(_snake_case		)

            lowerCAmelCase_       :						int							= importlib.import_module(f'.{module_name}'		,     """transformers.models"""		)
            try:
                return getattr(_snake_case		,     _snake_case		)
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_snake_case		,     """__name__"""		,     _snake_case		) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    lowerCAmelCase_       :						int							= importlib.import_module("""transformers"""		)
    if hasattr(_snake_case		,     _snake_case		):
        return getattr(_snake_case		,     _snake_case		)

    return None
def       __UpperCamelCase					(      lowercase__						:							int		,     lowercase__						:							Optional[Any] = None		,     lowercase__						:							str = False		,     lowercase__						:							Union[str, Any] = False		,     lowercase__						:							str = None		,     lowercase__						:							List[str] = None		,     lowercase__						:							List[str] = None		,     lowercase__						:							Any = False		,     **lowercase__						:							int		,     )						->   Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						List[Any]							= get_file_from_repo(
        _snake_case		,     _snake_case		,     cache_dir=_snake_case		,     force_download=_snake_case		,     resume_download=_snake_case		,     proxies=_snake_case		,     use_auth_token=_snake_case		,     revision=_snake_case		,     local_files_only=_snake_case		,     )
    if resolved_config_file is None:
        logger.info(
            """Could not locate the feature extractor configuration file, will try to use the model config instead."""		)
        return {}

    with open(_snake_case		,     encoding="""utf-8"""		) as reader:
        return json.load(_snake_case		)

class 		__a				:



       def __init__(  self						: str      ):
           raise EnvironmentError(
               """AutoFeatureExtractor is designed to be instantiated """
               """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""      )



       @classmethod
       @replace_list_option_in_docstrings(UpperCamelCase_      )
       def 			A				(  cls						: str       ,      UpperCAmelCase						: Any       ,      **UpperCAmelCase						: Optional[Any]      ):
           lowerCAmelCase_       :						str							= kwargs.pop("""config"""       ,      UpperCamelCase_      )
           lowerCAmelCase_       :						List[Any]							= kwargs.pop("""trust_remote_code"""       ,      UpperCamelCase_      )
           lowerCAmelCase_       :						Any							= True

           lowerCAmelCase_       :						Any							= FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_       ,      **UpperCamelCase_      )
           lowerCAmelCase_       :						Tuple							= config_dict.get("""feature_extractor_type"""       ,      UpperCamelCase_      )
           lowerCAmelCase_       :						Union[str, Any]							= None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""       ,      {}      ):
               lowerCAmelCase_       :						List[str]							= config_dict['auto_map']['AutoFeatureExtractor']

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(UpperCamelCase_       ,      UpperCamelCase_      ):
                   lowerCAmelCase_       :						List[Any]							= AutoConfig.from_pretrained(UpperCamelCase_       ,      **UpperCamelCase_      )
               # It could be in `config.feature_extractor_type``
               lowerCAmelCase_       :						List[Any]							= getattr(UpperCamelCase_       ,      """feature_extractor_type"""       ,      UpperCamelCase_      )
               if hasattr(UpperCamelCase_       ,      """auto_map"""      ) and "AutoFeatureExtractor" in config.auto_map:
                   lowerCAmelCase_       :						str							= config.auto_map['AutoFeatureExtractor']

           if feature_extractor_class is not None:
               lowerCAmelCase_       :						Tuple							= feature_extractor_class_from_name(UpperCamelCase_      )

           lowerCAmelCase_       :						int							= feature_extractor_auto_map is not None
           lowerCAmelCase_       :						Dict							= feature_extractor_class is not None or type(UpperCamelCase_      ) in FEATURE_EXTRACTOR_MAPPING
           lowerCAmelCase_       :						int							= resolve_trust_remote_code(
               UpperCamelCase_       ,      UpperCamelCase_       ,      UpperCamelCase_       ,      UpperCamelCase_      )

           if has_remote_code and trust_remote_code:
               lowerCAmelCase_       :						Dict							= get_class_from_dynamic_module(
                   UpperCamelCase_       ,      UpperCamelCase_       ,      **UpperCamelCase_      )
               lowerCAmelCase_       :						List[Any]							= kwargs.pop("""code_revision"""       ,      UpperCamelCase_      )
               if os.path.isdir(UpperCamelCase_      ):
                   feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(UpperCamelCase_       ,      **UpperCamelCase_      )
           elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(UpperCamelCase_       ,      **UpperCamelCase_      )
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(UpperCamelCase_      ) in FEATURE_EXTRACTOR_MAPPING:
               lowerCAmelCase_       :						List[Any]							= FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase_      )]
               return feature_extractor_class.from_dict(UpperCamelCase_       ,      **UpperCamelCase_      )

           raise ValueError(
               F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
               F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
               F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'      )



       @staticmethod
       def 			A				(  UpperCAmelCase						: List[str]       ,      UpperCAmelCase						: Optional[Any]      ):
           FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase_       ,      UpperCamelCase_      )




"""simple docstring"""



import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCamelCase__          =	{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_28,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class 				a			(					unittest.TestCase      ):



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      List[str]     							=					TOKEN
          HfFolder.save_token(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls      ):
          try:
              delete_repo(token=cls._token     ,	repo_id='test-config'      )
          except HTTPError:
              pass

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

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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('test-config'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[int]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

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

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

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('valid_org/test-config-org'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

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

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

          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          CustomConfig.register_for_auto_class()
          UpperCAmelCase__	:      Dict     							=					CustomConfig(attribute=42      )

          config.push_to_hub('test-dynamic-config'     ,	use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(config.auto_map     ,	{'AutoConfig': 'custom_configuration.CustomConfig'}      )

          UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''     ,	trust_remote_code=UpperCamelCase_      )
          # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
          self.assertEqual(new_config.__class__.__name__     ,	'CustomConfig'      )
          self.assertEqual(new_config.attribute     ,	42      )





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					GPTaConfig()

          # attempt to modify each of int/float/bool/str config records and verify they were updated
          UpperCAmelCase__	:      List[str]     							=					c.n_embd + 1  # int
          UpperCAmelCase__	:      Optional[int]     							=					c.resid_pdrop + 1.0  # float
          UpperCAmelCase__	:      Optional[int]     							=					not c.scale_attn_weights  # bool
          UpperCAmelCase__	:      str     							=					c.summary_type + 'foo'  # str
          c.update_from_string(
              F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''      )
          self.assertEqual(UpperCamelCase_     ,	c.n_embd     ,	'mismatch for key: n_embd'      )
          self.assertEqual(UpperCamelCase_     ,	c.resid_pdrop     ,	'mismatch for key: resid_pdrop'      )
          self.assertEqual(UpperCamelCase_     ,	c.scale_attn_weights     ,	'mismatch for key: scale_attn_weights'      )
          self.assertEqual(UpperCamelCase_     ,	c.summary_type     ,	'mismatch for key: summary_type'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					PretrainedConfig()
          UpperCAmelCase__	:      str     							=					[key for key in base_config.__dict__ if key not in config_common_kwargs]
          # If this part of the test fails, you have arguments to addin config_common_kwargs above.
          self.assertListEqual(
              UpperCamelCase_     ,	['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']      )
          UpperCAmelCase__	:      int     							=					[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_     ,	UpperCamelCase_      )]
          if len(UpperCamelCase_      ) > 0:
              raise ValueError(
                  'The following keys are set with the default values in'
                  ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                  F''' {', '.join(UpperCamelCase_      )}.'''      )



      def  __snake_case							(   self      ):
          with self.assertRaises(UpperCamelCase_      ):
              # config is in subfolder, the following should not work without specifying the subfolder
              UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'      )

          UpperCAmelCase__	:      str     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     ,	subfolder='bert'      )

          self.assertIsNotNone(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase__	:      str     							=					mock.Mock()
          UpperCAmelCase__	:      List[Any]     							=					500
          UpperCAmelCase__	:      Optional[int]     							=					{}
          UpperCAmelCase__	:      Dict     							=					HTTPError
          UpperCAmelCase__	:      Optional[int]     							=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )

          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('requests.Session.request'     ,	return_value=UpperCamelCase_      ) as mock_head:
              UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )
              # This check we did call the fake head request
              mock_head.assert_called()



      def  __snake_case							(   self      ):
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained(
              'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					AutoConfig.from_pretrained('bert-base-cased'      )
          UpperCAmelCase__	:      str     							=					['config.4.0.0.json']

          with tempfile.TemporaryDirectory() as tmp_dir:
              configuration.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					2
              json.dump(configuration.to_dict()     ,	open(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	'w'      )      )

              # This should pick the new configuration file as the version of Transformers is > 4.0.0
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	2      )

              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old configuration file as the version of Transformers is < 4.42.0
              UpperCAmelCase__	:      Tuple     							=					['config.42.0.0.json']
              UpperCAmelCase__	:      Tuple     							=					768
              configuration.save_pretrained(UpperCamelCase_      )
              shutil.move(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	os.path.join(UpperCamelCase_     ,	'config.42.0.0.json'      )      )
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	768      )



      def  __snake_case							(   self      ):
          # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
          UpperCAmelCase__	:      Tuple     							=					'hf-internal-testing/test-two-configs'

          import transformers as new_transformers

          UpperCAmelCase__	:      Optional[int]     							=					'v4.0.0'
          UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					new_transformers.models.auto.AutoConfig.from_pretrained(
              UpperCamelCase_     ,	return_unused_kwargs=UpperCamelCase_      )
          self.assertEqual(new_configuration.hidden_size     ,	2      )
          # This checks `_configuration_file` ia not kept in the kwargs by mistake.
          self.assertDictEqual(UpperCamelCase_     ,	{}      )

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          UpperCAmelCase__	:      List[Any]     							=					'v3.0.0'
          UpperCAmelCase__	:      Union[str, Any]     							=					old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_      )
          self.assertEqual(old_configuration.hidden_size     ,	768      )

