


'''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()
__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''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''',
}
__snake_case							      =     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 			a      (			__a ,					__a ,					__a ,					__a ,					__a   )  ->	Dict:



					'''simple docstring'''






					for attribute in key.split('''.'''   ):
										UpperCamelCase__   :int												= getattr(__a ,					__a   )

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

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


def 			a      (			__a ,					__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= []
					UpperCamelCase__   :str												= fairseq_model.state_dict()

					UpperCamelCase__   :List[Any]												= hf_model.feature_extractor
					UpperCamelCase__   :Dict												= hf_model.adapter

					for name, value in fairseq_dict.items():
										UpperCamelCase__   :Tuple												= False
										if "conv_layers" in name:
															load_conv_layer(
															    __a ,					__a ,					__a ,					__a ,					hf_model.config.feat_extract_norm == '''group''' ,					)
															UpperCamelCase__   :Any												= True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']   ):
															load_adapter(__a ,					__a ,					__a ,					__a   )
															UpperCamelCase__   :List[Any]												= True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''   )[-1] == name.split('''.'''   )[0]:
																									UpperCamelCase__   :Tuple												= True
																									if "*" in mapped_key:
																														UpperCamelCase__   :Any												= name.split(__a   )[0].split('''.'''   )[-2]
																														UpperCamelCase__   :Optional[Any]												= mapped_key.replace('''*''' ,					__a   )
																									if "weight_g" in name:
																														UpperCamelCase__   :List[Any]												= '''weight_g'''
																									elif "weight_v" in name:
																														UpperCamelCase__   :Optional[Any]												= '''weight_v'''
																									elif "bias" in name:
																														UpperCamelCase__   :Tuple												= '''bias'''
																									elif "weight" in name:
																														UpperCamelCase__   :Optional[Any]												= '''weight'''
																									else:
																														UpperCamelCase__   :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   )  ->	Optional[int]:



					'''simple docstring'''






					UpperCamelCase__   :int												= full_name.split('''conv_layers.'''   )[-1]
					UpperCamelCase__   :str												= name.split('''.'''   )
					UpperCamelCase__   :int												= int(items[0]   )
					UpperCamelCase__   :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.'''
															)
															UpperCamelCase__   :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.'''
															)
															UpperCamelCase__   :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."
															)
															UpperCamelCase__   :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.'''
															)
															UpperCamelCase__   :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 ,					__a ,					__a ,					__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= full_name.split('''adaptor.'''   )[-1]
					UpperCamelCase__   :int												= name.split('''.'''   )

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


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :Any												= emb.weight.shape
					UpperCamelCase__   :int												= nn.Linear(__a ,					__a ,					bias=__a   )
					UpperCamelCase__   :Dict												= emb.weight.data
					return lin_layer


@torch.no_grad()
def 			a      (			__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					)  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__   :str												= WavaVecaConfig.from_pretrained(
					    __a ,					add_adapter=__a ,					adapter_stride=__a ,					adapter_kernel_size=__a ,					use_auth_token=__a ,					output_hidden_size=__a ,					)
					UpperCamelCase__   :Dict												= MBartConfig.from_pretrained(__a   )

					# load model
					UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :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,
					    } ,					)
					UpperCamelCase__   :List[str]												= model[0].eval()

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

					# set weights for wav2vec2 encoder
					UpperCamelCase__   :List[str]												= WavaVecaModel(__a   )

					recursively_load_weights_wavaveca(model.encoder ,					__a   )

					# load decoder weights
					UpperCamelCase__   :Tuple												= MBartForCausalLM(__a   )
					UpperCamelCase__					,				UpperCamelCase__   :int												= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,					strict=__a   )
					logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''   )
					logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''   )

					UpperCamelCase__   :str												= SpeechEncoderDecoderModel(encoder=__a ,					decoder=__a   )
					UpperCamelCase__   :Tuple												= False

					UpperCamelCase__   :List[Any]												= MBartaaTokenizer(__a   )
					tokenizer.save_pretrained(__a   )

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

					UpperCamelCase__   :List[Any]												= tokenizer.eos_token_id
					UpperCamelCase__   :Optional[int]												= 250004
					UpperCamelCase__   :Tuple												= tokenizer.eos_token_id

					UpperCamelCase__   :int												= SpeechEncoderDecoderConfig.from_dict(__a   )

					hf_wavavec.save_pretrained(__a   )
					feature_extractor.save_pretrained(__a   )


if __name__ == "__main__":
		__snake_case							      =     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''')

		__snake_case							      =     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,
		)



'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .multicontrolnet import MultiControlNetModel
		from .pipeline_controlnet import StableDiffusionControlNetPipeline
		from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
		from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
		from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline