






import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4]
								__magic_name__		:Union[str, Any]        =      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								__magic_name__		:Optional[Any]        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
								__magic_name__		:str        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      '''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      ''''''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      (
								    '''It was the year of Our Lord one thousand seven hundred and '''
								    '''seventy-five\n\nSpiritual revelations were conceded to England '''
								    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
								)
								__magic_name__     ,				__magic_name__		:str        =      process_story(__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      [
								    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
								    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
								]
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      ['''It was the best of times.''']
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([1, 2, 3, 4]     )
								__magic_name__		:List[str]        =      torch.tensor([1, 1, 1, 1]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					0     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]     )
								__magic_name__		:Union[str, Any]        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					2_3     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([8, 2, 3, 4, 1, 1, 1]     )
								__magic_name__		:Dict        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					1     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0_1
								__magic_name__		:List[Any]        =      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]     )
								__magic_name__		:str        =      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]     )

								__magic_name__		:Tuple        =      compute_token_type_ids(__lowerCAmelCase       ,					__lowerCAmelCase     )
								np.testing.assert_array_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )














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()
SCREAMING_SNAKE_CASE__      :			int							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			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""",
}
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				for attribute in key.split('''.''' ):
								__magic_name__		:List[Any]        =      getattr(snake_case,  snake_case )

				if weight_type is not None:
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  snake_case ).shape
				else:
								__magic_name__		:List[Any]        =      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":
								__magic_name__		:Optional[Any]        =      value
				elif weight_type == "weight_g":
								__magic_name__		:Dict        =      value
				elif weight_type == "weight_v":
								__magic_name__		:Any        =      value
				elif weight_type == "bias":
								__magic_name__		:List[Any]        =      value
				else:
								__magic_name__		:Union[str, Any]        =      value

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

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      []
				__magic_name__		:Dict        =      fairseq_model.state_dict()

				__magic_name__		:Union[str, Any]        =      hf_model.feature_extractor
				__magic_name__		:Optional[int]        =      hf_model.adapter

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

				logger.warning(f'''Unused weights: {unused_weights}''' )

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      full_name.split('''conv_layers.''' )[-1]
				__magic_name__		:List[str]        =      name.split('''.''' )
				__magic_name__		:Optional[int]        =      int(items[0] )
				__magic_name__		:Dict        =      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.'''
												)
												__magic_name__		:Tuple        =      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.'''
												)
												__magic_name__		:List[Any]        =      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."
												)
												__magic_name__		:Dict        =      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.'''
												)
												__magic_name__		:Any        =      value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
				else:
								unused_weights.append(snake_case )

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      full_name.split('''adaptor.''' )[-1]
				__magic_name__		:Optional[Any]        =      name.split('''.''' )

				if items[1].isdigit():
								__magic_name__		:Tuple        =      int(items[1] )
				else:
								__magic_name__		:str        =      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.'''
																__magic_name__		:Optional[int]        =      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.'''
																__magic_name__		:Any        =      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.'''
																__magic_name__		:List[Any]        =      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.'''
																__magic_name__		:Union[str, Any]        =      value
																logger.info(f'''Adapter proj layer weight was initialized from {full_name}.''' )
				elif isinstance(snake_case,  snake_case ):
								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.'''
												__magic_name__		:Tuple        =      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.'''
												__magic_name__		:Optional[int]        =      value
												logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
				else:
								unused_weights.append(snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:int        =      emb.weight.shape
				__magic_name__		:Tuple        =      nn.Linear(snake_case,  snake_case,  bias=snake_case )
				__magic_name__		:Optional[int]        =      emb.weight.data
				return lin_layer

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  ):







				"""simple docstring"""






				__magic_name__		:Any        =      WavaVecaConfig.from_pretrained(
				    snake_case,  add_adapter=snake_case,  adapter_stride=snake_case,  adapter_kernel_size=snake_case,  use_auth_token=snake_case,  output_hidden_size=snake_case,  )
				__magic_name__		:Union[str, Any]        =      MBartConfig.from_pretrained(snake_case )

				# load model
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      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,
				    },  )
				__magic_name__		:Tuple        =      model[0].eval()

				# load feature extractor
				__magic_name__		:Optional[Any]        =      WavaVecaFeatureExtractor.from_pretrained(snake_case,  use_auth_token=snake_case )

				# set weights for wav2vec2 encoder
				__magic_name__		:str        =      WavaVecaModel(snake_case )

				recursively_load_weights_wavaveca(model.encoder,  snake_case )

				# load decoder weights
				__magic_name__		:int        =      MBartForCausalLM(snake_case )
				__magic_name__     ,				__magic_name__		:int        =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),  strict=snake_case )
				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}''' )

				__magic_name__		:Tuple        =      SpeechEncoderDecoderModel(encoder=snake_case,  decoder=snake_case )
				__magic_name__		:int        =      False

				__magic_name__		:int        =      MBartaaTokenizer(snake_case )
				tokenizer.save_pretrained(snake_case )

				__magic_name__		:Union[str, Any]        =      hf_wavavec.config.to_dict()
				__magic_name__		:Any        =      tokenizer.pad_token_id
				__magic_name__		:List[Any]        =      tokenizer.bos_token_id
				__magic_name__		:List[Any]        =      tokenizer.eos_token_id
				__magic_name__		:Any        =      '''mbart50'''
				__magic_name__		:Any        =      '''wav2vec2'''

				__magic_name__		:Dict        =      tokenizer.eos_token_id
				__magic_name__		:Optional[int]        =      2_5_0_0_0_4
				__magic_name__		:List[Any]        =      tokenizer.eos_token_id

				__magic_name__		:int        =      SpeechEncoderDecoderConfig.from_dict(snake_case )

				hf_wavavec.save_pretrained(snake_case )
				feature_extractor.save_pretrained(snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Any							=		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=10_24, type=int, help="""encoder output dim""")
					parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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,
					)






