


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


UpperCamelCase					:       Union[str, Any]			    =     get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCamelCase					:       List[Any]			    =     get_tests_dir("""fixtures/vocab.json""")
UpperCamelCase					:       Dict			    =     get_tests_dir("""fixtures""")



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Union[str, Any]     =   0

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[Any]     =   AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"       )
        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]       ):
        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  Optional[Any]     =   WavaVecaConfig()
              a__	:  Optional[int]     =   AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"       )

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

              a__	:  int     =   AutoProcessor.from_pretrained(lowerCamelCase__       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int       ):
        with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(lowerCamelCase__				,				os.path.join(lowerCamelCase__				,				lowerCamelCase__       )       )
              copyfile(lowerCamelCase__				,				os.path.join(lowerCamelCase__				,				"vocab.json"       )       )

              a__	:  List[str]     =   AutoProcessor.from_pretrained(lowerCamelCase__       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str       ):
        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  List[Any]     =   WavaVecaFeatureExtractor()
              a__	:  Optional[int]     =   AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"       )

              a__	:  Dict     =   WavaVecaProcessor(lowerCamelCase__				,				lowerCamelCase__       )

              # save in new folder
              processor.save_pretrained(lowerCamelCase__       )

              # drop `processor_class` in tokenizer
              with open(os.path.join(lowerCamelCase__				,				lowerCamelCase__       )				,				"r"       ) as f:
                    a__	:  Optional[Any]     =   json.load(lowerCamelCase__       )
                    config_dict.pop("processor_class"       )

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

              a__	:  Union[str, Any]     =   AutoProcessor.from_pretrained(lowerCamelCase__       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  Any     =   WavaVecaFeatureExtractor()
              a__	:  List[str]     =   AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"       )

              a__	:  Any     =   WavaVecaProcessor(lowerCamelCase__				,				lowerCamelCase__       )

              # save in new folder
              processor.save_pretrained(lowerCamelCase__       )

              # drop `processor_class` in feature extractor
              with open(os.path.join(lowerCamelCase__				,				lowerCamelCase__       )				,				"r"       ) as f:
                    a__	:  List[str]     =   json.load(lowerCamelCase__       )
                    config_dict.pop("processor_class"       )

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

              a__	:  Tuple     =   AutoProcessor.from_pretrained(lowerCamelCase__       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  Union[str, Any]     =   WavaVecaConfig(processor_class="Wav2Vec2Processor"       )
              model_config.save_pretrained(lowerCamelCase__       )
              # copy relevant files
              copyfile(lowerCamelCase__				,				os.path.join(lowerCamelCase__				,				"vocab.json"       )       )
              # create emtpy sample processor
              with open(os.path.join(lowerCamelCase__				,				lowerCamelCase__       )				,				"w"       ) as f:
                    f.write("{}"       )

              a__	:  int     =   AutoProcessor.from_pretrained(lowerCamelCase__       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

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

        a__	:  str     =   AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"				,				trust_remote_code=lowerCamelCase__       )
        self.assertTrue(processor.special_attribute_present       )
        self.assertEqual(processor.__class__.__name__				,				"NewProcessor"       )

        a__	:  List[str]     =   processor.feature_extractor
        self.assertTrue(feature_extractor.special_attribute_present       )
        self.assertEqual(feature_extractor.__class__.__name__				,				"NewFeatureExtractor"       )

        a__	:  List[Any]     =   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
              a__	:  Optional[int]     =   AutoProcessor.from_pretrained(
                  "hf-internal-testing/test_dynamic_processor"				,				trust_remote_code=lowerCamelCase__				,				use_fast=lowerCamelCase__       )
              a__	:  Optional[int]     =   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    _UpperCamelCase( self			:  List[Any]       ):
        try:
              AutoConfig.register("custom"				,				lowerCamelCase__       )
              AutoFeatureExtractor.register(lowerCamelCase__				,				lowerCamelCase__       )
              AutoTokenizer.register(lowerCamelCase__				,				slow_tokenizer_class=lowerCamelCase__       )
              AutoProcessor.register(lowerCamelCase__				,				lowerCamelCase__       )
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(lowerCamelCase__       ):
                    AutoProcessor.register(lowerCamelCase__				,				lowerCamelCase__       )

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

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

              a__	:  Dict     =   CustomProcessor(lowerCamelCase__				,				lowerCamelCase__       )

              with tempfile.TemporaryDirectory() as tmp_dir:
                    processor.save_pretrained(lowerCamelCase__       )
                    a__	:  Union[str, Any]     =   AutoProcessor.from_pretrained(lowerCamelCase__       )
                    self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

        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    _UpperCamelCase( self			:  Union[str, Any]       ):



        class A__     ( A__  ):



          """simple docstring"""



          _lowercase									=		False




        class A__     ( A__  ):



          """simple docstring"""



          _lowercase									=		False




        class A__     ( A__  ):



          """simple docstring"""



          _lowercase									=		'AutoFeatureExtractor'
          _lowercase									=		'AutoTokenizer'
          _lowercase									=		False

        try:
              AutoConfig.register("custom"				,				lowerCamelCase__       )
              AutoFeatureExtractor.register(lowerCamelCase__				,				lowerCamelCase__       )
              AutoTokenizer.register(lowerCamelCase__				,				slow_tokenizer_class=lowerCamelCase__       )
              AutoProcessor.register(lowerCamelCase__				,				lowerCamelCase__       )
              # If remote code is not set, the default is to use local classes.
              a__	:  int     =   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.
              a__	:  List[Any]     =   AutoProcessor.from_pretrained(
                  "hf-internal-testing/test_dynamic_processor"				,				trust_remote_code=lowerCamelCase__       )
              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.
              a__	:  List[Any]     =   AutoProcessor.from_pretrained(
                  "hf-internal-testing/test_dynamic_processor"				,				trust_remote_code=lowerCamelCase__       )
              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    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  str     =   AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"       )
        self.assertEqual(processor.__class__.__name__				,				"BertTokenizerFast"       )

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  str     =   AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"       )
        self.assertEqual(processor.__class__.__name__				,				"ConvNextImageProcessor"       )




@is_staging_test
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

  @classmethod
  def    _UpperCamelCase( cls			:  Optional[Any]       ):
        a__	:  Union[str, Any]     =   TOKEN
        HfFolder.save_token(lowerCamelCase__       )

  @classmethod
  def    _UpperCamelCase( cls			:  Tuple       ):
        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    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Optional[Any]     =   WavaVecaProcessor.from_pretrained(lowerCamelCase__       )
        with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(lowerCamelCase__				,				"test-processor"       )				,				push_to_hub=lowerCamelCase__				,				use_auth_token=self._token       )

              a__	:  List[str]     =   WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''       )
              for k, v in processor.feature_extractor.__dict__.items():
                    self.assertEqual(lowerCamelCase__				,				getattr(new_processor.feature_extractor				,				lowerCamelCase__       )       )
              self.assertDictEqual(new_processor.tokenizer.get_vocab()				,				processor.tokenizer.get_vocab()       )

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Union[str, Any]     =   WavaVecaProcessor.from_pretrained(lowerCamelCase__       )

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

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

  def    _UpperCamelCase( self			:  Optional[int]       ):
        CustomFeatureExtractor.register_for_auto_class()
        CustomTokenizer.register_for_auto_class()
        CustomProcessor.register_for_auto_class()

        a__	:  List[Any]     =   CustomFeatureExtractor.from_pretrained(lowerCamelCase__       )

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

        a__	:  List[Any]     =   CustomProcessor(lowerCamelCase__				,				lowerCamelCase__       )

        with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(f'''{USER}/test-dynamic-processor'''				,				token=self._token       )
              a__	:  Optional[Any]     =   Repository(lowerCamelCase__				,				clone_from=f'''{USER}/test-dynamic-processor'''				,				token=self._token       )
              processor.save_pretrained(lowerCamelCase__       )

              # 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(lowerCamelCase__				,				"tokenizer_config.json"       )       ) as f:
                    a__	:  Dict     =   json.load(lowerCamelCase__       )
              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(lowerCamelCase__				,				"custom_feature_extraction.py"       )       )       )
              self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,				"custom_tokenization.py"       )       )       )
              self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,				"custom_processing.py"       )       )       )

              repo.push_to_hub()

        a__	:  List[str]     =   AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''				,				trust_remote_code=lowerCamelCase__       )
        # 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"       )







'''simple docstring'''





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_		:     str          =		logging.get_logger(__name__)

UpperCamelCase_		:     Optional[int]          =		{
    '''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''',
}
UpperCamelCase_		:     int          =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]       )		->					int:

     """simple docstring"""




     for attribute in key.split("."       ):
          _snake_case				= getattr(_UpperCamelCase   ,		_UpperCamelCase       )

     if weight_type is not None:
          _snake_case				= getattr(_UpperCamelCase   ,		_UpperCamelCase       ).shape
     else:
          _snake_case				= 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":
          _snake_case				= value
     elif weight_type == "weight_g":
          _snake_case				= value
     elif weight_type == "weight_v":
          _snake_case				= value
     elif weight_type == "bias":
          _snake_case				= value
     else:
          _snake_case				= value

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





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       Tuple       )		->					str:

     """simple docstring"""




     _snake_case				= []
     _snake_case				= fairseq_model.state_dict()

     _snake_case				= hf_model.feature_extractor
     _snake_case				= hf_model.adapter

     for name, value in fairseq_dict.items():
          _snake_case				= False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		hf_model.config.feat_extract_norm == "group"   ,		)
               _snake_case				= True
          elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]       ):
               load_adapter(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
               _snake_case				= True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                         _snake_case				= True
                         if "*" in mapped_key:
                              _snake_case				= name.split(_UpperCamelCase       )[0].split("."       )[-2]
                              _snake_case				= mapped_key.replace("*"   ,		_UpperCamelCase       )
                         if "weight_g" in name:
                              _snake_case				= "weight_g"
                         elif "weight_v" in name:
                              _snake_case				= "weight_v"
                         elif "bias" in name:
                              _snake_case				= "bias"
                         elif "weight" in name:
                              _snake_case				= "weight"
                         else:
                              _snake_case				= None
                         set_recursively(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
                    continue
          if not is_used:
               unused_weights.append(_UpperCamelCase       )

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





def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Dict:

     """simple docstring"""




     _snake_case				= full_name.split("conv_layers."       )[-1]
     _snake_case				= name.split("."       )
     _snake_case				= int(items[0]       )
     _snake_case				= 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."""
               )
               _snake_case				= 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."""
               )
               _snake_case				= 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."
               )
               _snake_case				= 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."""
               )
               _snake_case				= value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
     else:
          unused_weights.append(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Tuple       )		->					Any:

     """simple docstring"""




     _snake_case				= full_name.split("adaptor."       )[-1]
     _snake_case				= name.split("."       )

     if items[1].isdigit():
          _snake_case				= int(items[1]       )
     else:
          _snake_case				= None

     if "adaptor" not in full_name:
          if "proj_ln" in full_name:
               # has to be layer norm
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.bias.data.shape
                    ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                    _snake_case				= value
                    logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""       )
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.weight.data.shape
                    ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                    _snake_case				= value
          else:
               # has to be projection layer
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj.bias.data.shape
                    ), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                    _snake_case				= value
                    logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""       )
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj.weight.data.shape
                    ), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                    _snake_case				= value
                    logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""       )
     elif isinstance(_UpperCamelCase   ,		_UpperCamelCase       ):
          if "bias" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.bias.data.shape
               ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
               _snake_case				= value
               logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
          elif "weight" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.weight.data.shape
               ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
               _snake_case				= value
               logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
     else:
          unused_weights.append(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       Tuple       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				,   _snake_case				= emb.weight.shape
     _snake_case				= nn.Linear(_UpperCamelCase   ,		_UpperCamelCase   ,		bias=_UpperCamelCase       )
     _snake_case				= emb.weight.data
     return lin_layer





@torch.no_grad()
def       __a      (  _UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		)		->					List[str]:

     """simple docstring"""




     _snake_case				= WavaVecaConfig.from_pretrained(
         _UpperCamelCase   ,		add_adapter=_UpperCamelCase   ,		adapter_stride=_UpperCamelCase   ,		adapter_kernel_size=_UpperCamelCase   ,		use_auth_token=_UpperCamelCase   ,		output_hidden_size=_UpperCamelCase   ,		)
     _snake_case				= MBartConfig.from_pretrained(_UpperCamelCase       )

     # load model
     _snake_case				,   _snake_case				,   _snake_case				= fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]   ,		arg_overrides={
             "config_yaml": config_yaml_path,
             "data": "/".join(dict_path.split("/"       )[:-1]       ),
             "w2v_path": checkpoint_path,
             "load_pretrained_decoder_from": None,
         }   ,		)
     _snake_case				= model[0].eval()

     # load feature extractor
     _snake_case				= WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase   ,		use_auth_token=_UpperCamelCase       )

     # set weights for wav2vec2 encoder
     _snake_case				= WavaVecaModel(_UpperCamelCase       )

     recursively_load_weights_wavaveca(model.encoder   ,		_UpperCamelCase       )

     # load decoder weights
     _snake_case				= MBartForCausalLM(_UpperCamelCase       )
     _snake_case				,   _snake_case				= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,		strict=_UpperCamelCase       )
     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}"""       )

     _snake_case				= SpeechEncoderDecoderModel(encoder=_UpperCamelCase   ,		decoder=_UpperCamelCase       )
     _snake_case				= False

     _snake_case				= MBartaaTokenizer(_UpperCamelCase       )
     tokenizer.save_pretrained(_UpperCamelCase       )

     _snake_case				= hf_wavavec.config.to_dict()
     _snake_case				= tokenizer.pad_token_id
     _snake_case				= tokenizer.bos_token_id
     _snake_case				= tokenizer.eos_token_id
     _snake_case				= "mbart50"
     _snake_case				= "wav2vec2"

     _snake_case				= tokenizer.eos_token_id
     _snake_case				= 250_004
     _snake_case				= tokenizer.eos_token_id

     _snake_case				= SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase       )

     hf_wavavec.save_pretrained(_UpperCamelCase       )
     feature_extractor.save_pretrained(_UpperCamelCase       )


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		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('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
   parser.add_argument(
       '''--encoder_config_path''',
       default='''facebook/wav2vec2-xls-r-1b''',
       type=str,
       help='''Path to hf encoder wav2vec2 checkpoint config''',
   )
   parser.add_argument(
       '''--decoder_config_path''',
       default='''facebook/mbart-large-50-one-to-many-mmt''',
       type=str,
       help='''Path to hf decoder checkpoint config''',
   )
   parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
   parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
   parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
   parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
   parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''')

   UpperCamelCase_		:     Tuple          =		parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       args.config_yaml_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       add_adapter=args.add_adapter,
       adapter_kernel_size=args.adapter_kernel_size,
       adapter_stride=args.adapter_stride,
       decoder_start_token_id=args.start_token_id,
       encoder_output_dim=args.encoder_output_dim,
   )


