





"""simple docstring"""




from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


lowerCamelCase_	:		List[Any]         =   {
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_15]},
}





@pytest.mark.parametrize(
    'task_cls'					,   [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]					,   )
def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			str									=    task_cls()
  A_    :			Optional[int]									=    asdict(_UpperCAmelCase   )
  A_    :			Union[str, Any]									=    task_template_from_dict(_UpperCAmelCase   )
  assert task == reloaded

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    LanguageModeling(text_column='input_text'    )
        assert {"input_text": "text"} == task.column_mapping

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    Features({'text': Value('string'    )}    )
        A_    :			Optional[int]									=    {'text_column': 'input_text'}
        A_    :			List[str]									=    LanguageModeling.from_dict(snake_case_    )
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    sorted(['pos', 'neg']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    TextClassification(text_column='input_text'   , label_column='input_label'    )
        self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    Features({'text': Value('string'    )}    )
        # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
        A_    :			List[str]									=    Features({'labels': ClassLabel}    )
        A_    :			List[str]									=    {'text_column': 'input_text', 'label_column': 'input_labels'}
        A_    :			List[str]									=    TextClassification.from_dict(snake_case_    )
        self.assertEqual('text-classification'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    TextClassification(text_column='input_text'   , label_column='input_label'    )
        self.assertEqual(task.label_schema['labels']   , snake_case_    )
        A_    :			Optional[int]									=    task.align_with_features(Features({'input_label': ClassLabel(names=self.labels    )}    )    )
        self.assertEqual(task.label_schema['labels']   , ClassLabel(names=self.labels    )    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    QuestionAnsweringExtractive(
            context_column='input_context'   , question_column='input_question'   , answers_column='input_answers'    )
        self.assertDictEqual(
            {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    Features({'question': Value('string'    ), 'context': Value('string'    )}    )
        A_    :			Tuple									=    Features(
            {
                'answers': Sequence(
                    {
                        'text': Value('string'    ),
                        'answer_start': Value('int32'    ),
                    }    )
            }    )
        A_    :			int									=    {
            'context_column': 'input_input_context',
            'question_column': 'input_question',
            'answers_column': 'input_answers',
        }
        A_    :			Any									=    QuestionAnsweringExtractive.from_dict(snake_case_    )
        self.assertEqual('question-answering-extractive'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    Summarization(text_column='input_text'   , summary_column='input_summary'    )
        self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    Features({'text': Value('string'    )}    )
        A_    :			Any									=    Features({'summary': Value('string'    )}    )
        A_    :			List[str]									=    {'text_column': 'input_text', 'summary_column': 'input_summary'}
        A_    :			Tuple									=    Summarization.from_dict(snake_case_    )
        self.assertEqual('summarization'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    AutomaticSpeechRecognition(audio_column='input_audio'   , transcription_column='input_transcription'    )
        self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    Features({'audio': Audio()}    )
        A_    :			List[Any]									=    Features({'transcription': Value('string'    )}    )
        A_    :			Tuple									=    {
            'audio_column': 'input_audio',
            'transcription_column': 'input_transcription',
        }
        A_    :			Any									=    AutomaticSpeechRecognition.from_dict(snake_case_    )
        self.assertEqual('automatic-speech-recognition'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    sorted(['pos', 'neg']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    AudioClassification(audio_column='input_audio'   , label_column='input_label'    )
        self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    Features({'audio': Audio()}    )
        A_    :			Optional[Any]									=    Features({'labels': ClassLabel}    )
        A_    :			Optional[Any]									=    {
            'audio_column': 'input_image',
            'label_column': 'input_label',
        }
        A_    :			Optional[Any]									=    AudioClassification.from_dict(snake_case_    )
        self.assertEqual('audio-classification'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    AudioClassification(audio_column='input_audio'   , label_column='input_label'    )
        self.assertEqual(task.label_schema['labels']   , snake_case_    )
        A_    :			Optional[Any]									=    task.align_with_features(Features({'input_label': ClassLabel(names=self.labels    )}    )    )
        self.assertEqual(task.label_schema['labels']   , ClassLabel(names=self.labels    )    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    sorted(['pos', 'neg']    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    ImageClassification(image_column='input_image'   , label_column='input_label'    )
        self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}   , task.column_mapping    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    Features({'image': Image()}    )
        A_    :			List[Any]									=    Features({'labels': ClassLabel}    )
        A_    :			Any									=    {
            'image_column': 'input_image',
            'label_column': 'input_label',
        }
        A_    :			int									=    ImageClassification.from_dict(snake_case_    )
        self.assertEqual('image-classification'   , task.task    )
        self.assertEqual(snake_case_   , task.input_schema    )
        self.assertEqual(snake_case_   , task.label_schema    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    ImageClassification(image_column='input_image'   , label_column='input_label'    )
        self.assertEqual(task.label_schema['labels']   , snake_case_    )
        A_    :			Dict									=    task.align_with_features(Features({'input_label': ClassLabel(names=self.labels    )}    )    )
        self.assertEqual(task.label_schema['labels']   , ClassLabel(names=self.labels    )    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    DatasetInfo(task_templates=QuestionAnsweringExtractive()    )
        A_    :			Dict									=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}   , info=snake_case_    )
        assert isinstance(dataset.info.task_templates   , snake_case_    )
        assert len(dataset.info.task_templates    ) == 1

        def keep_task(snake_case_    ):
          return x

        def dont_keep_task(snake_case_    ):
          A_    :			int									=    deepcopy(snake_case_    )
          A_    :			Optional[Any]									=    0
          return out

        A_    :			List[str]									=    dataset.map(snake_case_    )
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        A_    :			Tuple									=    dataset.map(snake_case_    )
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        A_    :			str									=    dataset.map(snake_case_    )
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        A_    :			Optional[Any]									=    dataset.map(snake_case_    )
        assert mapped_dataset.info.task_templates == []

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    Features({'text': Value('string'    ), 'label': ClassLabel(names=('pos', 'neg')    )}    )
        A_    :			str									=    TextClassification(text_column='text'   , label_column='label'    )
        A_    :			List[str]									=    DatasetInfo(features=snake_case_   , task_templates=snake_case_    )
        A_    :			Tuple									=    Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}   , info=snake_case_    )

        def process(snake_case_    ):
          return example

        A_    :			Optional[Any]									=    dataset.remove_columns('label'    )
        A_    :			str									=    modified_dataset.map(snake_case_    )
        assert mapped_dataset.info.task_templates == []







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, 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',
    'mask_emb': 'masked_spec_embed',
}
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			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":
      lowercase__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase__    :						Optional[int]       =			False
      if "conv_layers" in name:
         load_conv_layer(
             A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == """group"""		,					)
         lowercase__    :						Optional[int]       =			True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase__    :						Dict       =			mapped_key

            if key in name:
               lowercase__    :						int       =			True
               if "*" in mapped_key:
                  lowercase__    :						Any       =			name.split(A       )[0].split("""."""       )[-2]
                  lowercase__    :						int       =			mapped_key.replace("""*"""		,					A       )
               if "weight_g" in name:
                  lowercase__    :						Dict       =			"""weight_g"""
               elif "weight_v" in name:
                  lowercase__    :						Dict       =			"""weight_v"""
               elif "weight" in name:
                  lowercase__    :						Union[str, Any]       =			"""weight"""
               elif "bias" in name:
                  lowercase__    :						Optional[int]       =			"""bias"""
               else:
                  lowercase__    :						List[str]       =			None
               set_recursively(A		,					A		,					A		,					A		,					A       )
            continue
      if not is_used:
         unused_weights.append(A       )

   logger.warning(F'''Unused weights: {unused_weights}'''       )
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			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.'''
         )
         lowercase__    :						int       =			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.'''
         )
         lowercase__    :						str       =			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."
         )
         lowercase__    :						Any       =			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.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(A       )
def    a__    (	A       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


