# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[str]         =				data







				def __iter__(						self ):



						"""simple docstring"""





						for element in self.data:
								yield element




def     UpperCamelCase						(	__lowercase						:			int=True     ):
		'''simple docstring'''




		A_		:    Tuple         =				Accelerator(even_batches=__lowercase     )
		assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
		return accelerator




def     UpperCamelCase						(	__lowercase						:			Accelerator						,       __lowercase						:			int						,       __lowercase						:			int						,       __lowercase						:			bool = False     ):
		'''simple docstring'''




		if iterable:
				A_		:    List[Any]         =				DummyIterableDataset(torch.as_tensor(range(__lowercase     )     )     )
		else:
				A_		:    Tuple         =				TensorDataset(torch.as_tensor(range(__lowercase     )     )     )

		A_		:    List[Any]         =				DataLoader(__lowercase						,       batch_size=__lowercase     )
		A_		:    Union[str, Any]         =				accelerator.prepare(__lowercase     )

		return dl




def     UpperCamelCase						(	__lowercase						:			Accelerator						,       __lowercase						:			int						,       __lowercase						:			int						,       __lowercase						:			List[int]						,       __lowercase						:			List[int]						,       ):
		'''simple docstring'''




		A_		:    List[str]         =				create_dataloader(accelerator=__lowercase						,       dataset_size=__lowercase						,       batch_size=__lowercase     )

		A_		:    Any         =				[len(batch[0]     ) for batch in dl]

		if accelerator.process_index == 0:
				assert batch_sizes == process_0_expected_batch_sizes
		elif accelerator.process_index == 1:
				assert batch_sizes == process_1_expected_batch_sizes




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				create_accelerator()

		# without padding, we would expect a different number of batches
		verify_dataloader_batch_sizes(
		    __lowercase						,       dataset_size=3						,       batch_size=1						,       process_0_expected_batch_sizes=[1, 1]						,       process_1_expected_batch_sizes=[1, 1]						,       )

		# without padding, we would expect the same number of batches, but different sizes
		verify_dataloader_batch_sizes(
		    __lowercase						,       dataset_size=7						,       batch_size=2						,       process_0_expected_batch_sizes=[2, 2]						,       process_1_expected_batch_sizes=[2, 2]						,       )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				create_accelerator(even_batches=__lowercase     )

		verify_dataloader_batch_sizes(
		    __lowercase						,       dataset_size=3						,       batch_size=1						,       process_0_expected_batch_sizes=[1, 1]						,       process_1_expected_batch_sizes=[1]						,       )

		verify_dataloader_batch_sizes(
		    __lowercase						,       dataset_size=7						,       batch_size=2						,       process_0_expected_batch_sizes=[2, 2]						,       process_1_expected_batch_sizes=[2, 1]						,       )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				create_accelerator(even_batches=__lowercase     )

		A_		:    List[str]         =				torch.nn.Linear(1						,       1     )
		A_		:    Tuple         =				accelerator.prepare(__lowercase     )

		A_		:    str         =				create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1     )

		A_		:    Tuple         =				[]
		with accelerator.join_uneven_inputs([ddp_model]     ):
				for batch_idx, batch in enumerate(__lowercase     ):
						A_		:    Optional[int]         =				ddp_model(batch[0].float()     )
						A_		:    str         =				output.sum()
						loss.backward()
						batch_idxs.append(__lowercase     )

		accelerator.wait_for_everyone()

		if accelerator.process_index == 0:
				assert batch_idxs == [0, 1]
		elif accelerator.process_index == 1:
				assert batch_idxs == [0]




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		with warnings.catch_warnings(record=__lowercase     ) as w:
				with accelerator.join_uneven_inputs([Mock()]     ):
						pass

				assert issubclass(w[-1].category						,       __lowercase     )
				assert "only supported for multi-GPU" in str(w[-1].message     )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[Any]         =				True
		A_		:    Union[str, Any]         =				False
		A_		:    List[Any]         =				create_accelerator(even_batches=__lowercase     )
		A_		:    List[str]         =				torch.nn.Linear(1						,       1     )
		A_		:    Optional[int]         =				accelerator.prepare(__lowercase     )
		A_		:    List[str]         =				create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1     )
		A_		:    int         =				create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1     )

		with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__lowercase     ):
				A_		:    List[Any]         =				train_dl.batch_sampler.even_batches
				A_		:    Union[str, Any]         =				valid_dl.batch_sampler.even_batches

		assert train_dl_overridden_value == overridden_even_batches
		assert valid_dl_overridden_value == overridden_even_batches
		assert train_dl.batch_sampler.even_batches == default_even_batches
		assert valid_dl.batch_sampler.even_batches == default_even_batches




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				True
		A_		:    List[str]         =				False
		A_		:    List[str]         =				create_accelerator(even_batches=__lowercase     )
		A_		:    Optional[int]         =				torch.nn.Linear(1						,       1     )
		A_		:    List[Any]         =				accelerator.prepare(__lowercase     )
		create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1						,       iterable=__lowercase     )
		A_		:    int         =				create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1     )

		with warnings.catch_warnings():
				warnings.filterwarnings('ignore'     )
				try:
						with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__lowercase     ):
								A_		:    str         =				batch_dl.batch_sampler.even_batches
				except AttributeError:
						# ensure attribute error is not raised when processing iterable dl
						raise AssertionError

		assert batch_dl_overridden_value == overridden_even_batches
		assert batch_dl.batch_sampler.even_batches == default_even_batches




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    List[Any]         =				create_accelerator()
		A_		:    Union[str, Any]         =				torch.nn.Linear(1						,       1     )
		A_		:    str         =				accelerator.prepare(__lowercase     )
		create_dataloader(__lowercase						,       dataset_size=3						,       batch_size=1						,       iterable=__lowercase     )

		with warnings.catch_warnings(record=__lowercase     ) as w:
				with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__lowercase     ):
						pass

				assert issubclass(w[-1].category						,       __lowercase     )
				assert "only supported for map-style datasets" in str(w[-1].message     )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				create_accelerator()

		accelerator.print('Test that even_batches variable ensures uniform batches across processes'     )
		test_default_ensures_even_batch_sizes()

		accelerator.print('Run tests with even_batches disabled'     )
		test_can_disable_even_batches()

		accelerator.print('Test joining uneven inputs'     )
		test_can_join_uneven_inputs()

		accelerator.print('Test overriding even_batches when joining uneven inputs'     )
		test_join_can_override_even_batches()

		accelerator.print('Test overriding even_batches for mixed dataloader types'     )
		test_join_can_override_for_mixed_type_dataloaders()

		accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'     )
		test_join_raises_warning_for_iterable_when_overriding_even_batches()

		accelerator.print('Test join with non DDP distributed raises warning'     )
		A_		:    str         =				accelerator.state.distributed_type
		A_		:    Any         =				DistributedType.FSDP
		test_join_raises_warning_for_non_ddp_distributed(__lowercase     )
		A_		:    Tuple         =				original_state


if __name__ == "__main__":
	main()







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            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """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            =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Tuple						,__lowercase						:			str						,__lowercase						:			List[Any]						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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

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

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




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				[]
		A_		:    int         =				fairseq_model.state_dict()

		A_		:    Optional[Any]         =				hf_model.feature_extractor
		A_		:    List[str]         =				hf_model.adapter

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

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




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Tuple						,__lowercase						:			Dict						,__lowercase						:			Any     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Tuple						,__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




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

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




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_       ,      A_		:    Any         =				emb.weight.shape
		A_		:    Tuple         =				nn.Linear(__lowercase						,__lowercase						,bias=__lowercase     )
		A_		:    Optional[Any]         =				emb.weight.data
		return lin_layer




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Optional[int]						,__lowercase						:			Any						,__lowercase						:			str						,__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			List[Any]						,__lowercase						:			str						,):
		'''simple docstring'''




		A_		:    Optional[int]         =				WavaVecaConfig.from_pretrained(
		    __lowercase						,add_adapter=__lowercase						,adapter_stride=__lowercase						,adapter_kernel_size=__lowercase						,use_auth_token=__lowercase						,output_hidden_size=__lowercase						,)
		A_		:    Any         =				MBartConfig.from_pretrained(__lowercase     )

		# load model
		A_       ,      A_       ,      A_		:    Union[str, Any]         =				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,
		    }						,)
		A_		:    Union[str, Any]         =				model[0].eval()

		# load feature extractor
		A_		:    Any         =				WavaVecaFeatureExtractor.from_pretrained(__lowercase						,use_auth_token=__lowercase     )

		# set weights for wav2vec2 encoder
		A_		:    Optional[Any]         =				WavaVecaModel(__lowercase     )

		recursively_load_weights_wavaveca(model.encoder						,__lowercase     )

		# load decoder weights
		A_		:    Dict         =				MBartForCausalLM(__lowercase     )
		A_       ,      A_		:    Union[str, Any]         =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=__lowercase     )
		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}'''     )

		A_		:    Optional[int]         =				SpeechEncoderDecoderModel(encoder=__lowercase						,decoder=__lowercase     )
		A_		:    Any         =				False

		A_		:    List[Any]         =				MBartaaTokenizer(__lowercase     )
		tokenizer.save_pretrained(__lowercase     )

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

		A_		:    int         =				tokenizer.eos_token_id
		A_		:    List[str]         =				25_00_04
		A_		:    int         =				tokenizer.eos_token_id

		A_		:    Optional[Any]         =				SpeechEncoderDecoderConfig.from_dict(__lowercase     )

		hf_wavavec.save_pretrained(__lowercase     )
		feature_extractor.save_pretrained(__lowercase     )


if __name__ == "__main__":
	_UpperCAmelCase            =		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            =		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,
	)






