




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)

a_   		=     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_   		=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      ).shape
    else:
        __UpperCamelCase  		=						hf_pointer.shape

    assert hf_shape == value.shape, (
        F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        F" {value.shape} for {full_name}"
    )

    if weight_type == "weight":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_g":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_v":
        __UpperCamelCase  		=						value
    elif weight_type == "bias":
        __UpperCamelCase  		=						value
    else:
        __UpperCamelCase  		=						value

    logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.feature_extractor

    # if encoder has different dim to decoder -> use proj_weight
    __UpperCamelCase  		=						None

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        elif name.split('''.'''      )[0] == "proj":
            __UpperCamelCase  		=						fairseq_model.proj
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
                    __UpperCamelCase  		=						True
                    if "*" in mapped_key:
                        __UpperCamelCase  		=						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase  		=						'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase  		=						'''weight_v'''
                    elif "bias" in name:
                        __UpperCamelCase  		=						'''bias'''
                    elif "weight" in name:
                        __UpperCamelCase  		=						'''weight'''
                    else:
                        __UpperCamelCase  		=						None
                    set_recursively(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
                continue
        if not is_used:
            unused_weights.append(lowercase_      )

    logger.warning(F"Unused weights: {unused_weights}"      )

    return proj_weight




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						full_name.split('''conv_layers.'''      )[-1]
    __UpperCamelCase  		=						name.split('''.'''      )
    __UpperCamelCase  		=						int(items[0]      )
    __UpperCamelCase  		=						int(items[1]      )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                " found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						emb.weight.shape
    __UpperCamelCase  		=						nn.Linear(lowercase_					,     lowercase_					,     bias=lowercase_      )
    __UpperCamelCase  		=						emb.weight.data
    return lin_layer




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    with open(lowercase_					,     '''r'''					,     encoding='''utf-8'''      ) as f:
        __UpperCamelCase  		=						f.readlines()
        __UpperCamelCase  		=						[line.split(''' '''      )[0] for line in lines]

    __UpperCamelCase  		=						len(lowercase_      )

    __UpperCamelCase  		=						{
        '''<s>''': 0,
        '''<pad>''': 1,
        '''</s>''': 2,
        '''<unk>''': 3,
    }

    vocab_dict.update(dict(zip(lowercase_					,     range(4					,     num_words + 4      )      )      )      )
    return vocab_dict




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						WavaVecaConfig.from_pretrained(lowercase_      )
    __UpperCamelCase  		=						SpeechaTextaConfig.from_pretrained(
        lowercase_					,     vocab_size=lowercase_					,     decoder_layers=lowercase_					,     do_stable_layer_norm=lowercase_      )

    __UpperCamelCase  		=						WavaVecaFeatureExtractor(
        feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=lowercase_					,     return_attention_mask=lowercase_					,     )

    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]					,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
    __UpperCamelCase  		=						model[0].eval()

    # set weights for wav2vec2 encoder
    __UpperCamelCase  		=						WavaVecaModel(lowercase_      )
    __UpperCamelCase  		=						recursively_load_weights_wavaveca(model.encoder					,     lowercase_      )

    __UpperCamelCase  		=						SpeechaTextaForCausalLM(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,     strict=lowercase_      )

    # set output linear layer
    unexpected_keys.remove('''embed_out'''      )
    __UpperCamelCase  		=						nn.Parameter(model.decoder.embed_out.detach()      )

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"      )
    logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"      )

    __UpperCamelCase  		=						SpeechEncoderDecoderModel(encoder=lowercase_					,     decoder=lowercase_      )
    __UpperCamelCase  		=						False

    # add projection layer
    __UpperCamelCase  		=						nn.Parameter(projection_layer.weight      )
    __UpperCamelCase  		=						nn.Parameter(projection_layer.bias      )

    __UpperCamelCase  		=						create_vocab_dict(lowercase_      )

    with open(os.path.join(lowercase_					,     '''vocab.json'''      )					,     '''w'''      ) as fp:
        json.dump(lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						SpeechaTextaTokenizer(os.path.join(lowercase_					,     '''vocab.json'''      )      )
    tokenizer.save_pretrained(lowercase_      )

    __UpperCamelCase  		=						hf_wavavec.config.to_dict()
    __UpperCamelCase  		=						tokenizer.pad_token_id
    __UpperCamelCase  		=						tokenizer.bos_token_id
    __UpperCamelCase  		=						tokenizer.eos_token_id
    __UpperCamelCase  		=						'''speech_to_text_2'''
    __UpperCamelCase  		=						'''wav2vec2'''

    __UpperCamelCase  		=						SpeechEncoderDecoderConfig.from_dict(lowercase_      )

    hf_wavavec.save_pretrained(lowercase_      )
    feature_extractor.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument(
     "--encoder_config_path",
     default="facebook/wav2vec2-large-lv60",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/s2t-small-mustc-en-fr-st",
     type=str,
     help="Path to hf decoder s2t checkpoint config",
 )
 parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
 parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

 a_   		=     parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )












import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


a_   		=     get_tests_dir("fixtures/dummy_feature_extractor_config.json")
a_   		=     get_tests_dir("fixtures/vocab.json")
a_   		=     get_tests_dir("fixtures")

class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						0







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)
          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig()
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              # save in new folder
              model_config.save_pretrained(snake_case				)
              processor.save_pretrained(snake_case				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Any				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       snake_case				)				)
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

              # drop `processor_class` in tokenizer
              with open(os.path.join(snake_case ,       snake_case				) ,       '''r'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
                  config_dict.pop('''processor_class'''				)

              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write(json.dumps(snake_case				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

              # drop `processor_class` in feature extractor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''r'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
                  config_dict.pop('''processor_class'''				)

              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write(json.dumps(snake_case				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Dict				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig(processor_class='''Wav2Vec2Processor'''				)
              model_config.save_pretrained(snake_case				)
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)
              # create emtpy sample processor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write('''{}'''				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: int				):
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)

          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
          self.assertTrue(processor.special_attribute_present				)
          self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)

          __UpperCamelCase  		=						processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present				)
          self.assertEqual(feature_extractor.__class__.__name__ ,       '''NewFeatureExtractor'''				)

          __UpperCamelCase  		=						processor.tokenizer
          self.assertTrue(tokenizer.special_attribute_present				)
          if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__ ,       '''NewTokenizerFast'''				)

              # Test we can also load the slow version
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case ,       use_fast=snake_case				)
              __UpperCamelCase  		=						new_processor.tokenizer
              self.assertTrue(new_tokenizer.special_attribute_present				)
              self.assertEqual(new_tokenizer.__class__.__name__ ,       '''NewTokenizer'''				)
          else:
              self.assertEqual(tokenizer.__class__.__name__ ,       '''NewTokenizer'''				)







      def       snake_case	(     self					: List[Any]				):
          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(snake_case				):
                  AutoProcessor.register(snake_case ,       snake_case				)

              # Now that the config is registered, it can be used as any other config with the auto-API
              __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  __UpperCamelCase  		=						os.path.join(snake_case ,       '''vocab.txt'''				)
                  with open(snake_case ,       '''w''' ,       encoding='''utf-8'''				) as vocab_writer:
                      vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
                  __UpperCamelCase  		=						CustomTokenizer(snake_case				)

              __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(snake_case				)
                  __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)
                  self.assertIsInstance(snake_case ,       snake_case				)

          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]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]







      def       snake_case	(     self					: Optional[int]				):

          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Any    =					"AutoFeatureExtractor"
                lowerCAmelCase__			: str    =					"AutoTokenizer"
                lowerCAmelCase__			: str    =					False

          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # If remote code is not set, the default is to use local classes.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertFalse(processor.special_attribute_present				)
              self.assertFalse(processor.feature_extractor.special_attribute_present				)
              self.assertFalse(processor.tokenizer.special_attribute_present				)

              # If remote code is disabled, we load the local ones.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertFalse(processor.special_attribute_present				)
              self.assertFalse(processor.feature_extractor.special_attribute_present				)
              self.assertFalse(processor.tokenizer.special_attribute_present				)

              # If remote is enabled, we load from the Hub.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertTrue(processor.special_attribute_present				)
              self.assertTrue(processor.feature_extractor.special_attribute_present				)
              self.assertTrue(processor.tokenizer.special_attribute_present				)

          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]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''BertTokenizerFast'''				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''ConvNextImageProcessor'''				)







@is_staging_test
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      @classmethod
      def       snake_case	(     cls					: List[str]				):
          __UpperCamelCase  		=						TOKEN
          HfFolder.save_token(snake_case				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any]				):
          try:
              delete_repo(token=cls._token ,       repo_id='''test-processor'''				)
          except HTTPError:
              pass

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

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







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token				)

              __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"				)
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(snake_case ,       getattr(new_processor.feature_extractor ,       snake_case				)				)
              self.assertDictEqual(new_processor.tokenizer.get_vocab() ,       processor.tokenizer.get_vocab()				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor-org'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token ,       organization='''valid_org''' ,       )

              __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''				)
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(snake_case ,       getattr(new_processor.feature_extractor ,       snake_case				)				)
              self.assertDictEqual(new_processor.tokenizer.get_vocab() ,       processor.tokenizer.get_vocab()				)







      def       snake_case	(     self					: List[str]				):
          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              __UpperCamelCase  		=						os.path.join(snake_case ,       '''vocab.txt'''				)
              with open(snake_case ,       '''w''' ,       encoding='''utf-8'''				) as vocab_writer:
                  vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
              __UpperCamelCase  		=						CustomTokenizer(snake_case				)

          __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F"{USER}/test-dynamic-processor" ,       token=self._token				)
              __UpperCamelCase  		=						Repository(snake_case ,       clone_from=F"{USER}/test-dynamic-processor" ,       token=self._token				)
              processor.save_pretrained(snake_case				)

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

              # This has added the proper auto_map field to the tokenizer config
              with open(os.path.join(snake_case ,       '''tokenizer_config.json'''				)				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
              self.assertDictEqual(
                  tokenizer_config['''auto_map'''] ,       {
                      '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                      '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                  } ,       )

              # The code has been copied from fixtures
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_feature_extraction.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_tokenization.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_processing.py'''				)				)				)

              repo.push_to_hub()

          __UpperCamelCase  		=						AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor" ,       trust_remote_code=snake_case				)
          # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
          self.assertEqual(new_processor.__class__.__name__ ,       '''CustomProcessor'''				)






