

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()
A_       : List[str]         =			logging.get_logger(__name__)

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

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: int    ,      lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Dict					)     ->      int:
	for attribute in key.split("""."""					):
		A__							:    Tuple        =		getattr(lowercase_    ,      lowercase_					)

	if weight_type is not None:
		A__							:    Optional[int]        =		getattr(lowercase_    ,      lowercase_					).shape
	else:
		A__							:    List[str]        =		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__							:    Optional[int]        =		value
	elif weight_type == "weight_g":
		A__							:    List[Any]        =		value
	elif weight_type == "weight_v":
		A__							:    Optional[int]        =		value
	elif weight_type == "bias":
		A__							:    Optional[Any]        =		value
	else:
		A__							:    str        =		value

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

def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Tuple					)     ->      List[Any]:
	A__							:    List[str]        =		[]
	A__							:    Optional[Any]        =		fairseq_model.state_dict()

	A__							:    Any        =		hf_model.feature_extractor
	A__							:    Optional[int]        =		hf_model.adapter

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

	if items[1].isdigit():
		A__							:    str        =		int(items[1]					)
	else:
		A__							:    Dict        =		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__							:    Tuple        =		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__							:    str        =		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__							:    Union[str, Any]        =		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__							:    str        =		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__							:    List[Any]        =		value
			logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""					)
	else:
		unused_weights.append(lowercase_					)

def 						UpperCamelCase					(lowercase_: str					)     ->      Union[str, Any]:
	A__   ,   A__							:    List[str]        =		emb.weight.shape
	A__							:    Union[str, Any]        =		nn.Linear(lowercase_    ,      lowercase_    ,      bias=lowercase_					)
	A__							:    Optional[Any]        =		emb.weight.data
	return lin_layer

@torch.no_grad()
def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: Tuple    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[int]    ,      lowercase_: Tuple    ,      lowercase_: Optional[Any]    ,      lowercase_: str    ,      lowercase_: str    ,      lowercase_: Dict    ,      lowercase_: List[str]    ,      lowercase_: Tuple    ,      )     ->      Union[str, Any]:
	A__							:    Optional[Any]        =		WavaVecaConfig.from_pretrained(
	    lowercase_    ,      add_adapter=lowercase_    ,      adapter_stride=lowercase_    ,      adapter_kernel_size=lowercase_    ,      use_auth_token=lowercase_    ,      output_hidden_size=lowercase_    ,      )
	A__							:    List[str]        =		MBartConfig.from_pretrained(lowercase_					)

	# load model
	A__   ,   A__   ,   A__							:    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__							:    Tuple        =		model[0].eval()

	# load feature extractor
	A__							:    str        =		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__							:    Optional[int]        =		MBartForCausalLM(lowercase_					)
	A__   ,   A__							:    List[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__							:    Union[str, Any]        =		SpeechEncoderDecoderModel(encoder=lowercase_    ,      decoder=lowercase_					)
	A__							:    Optional[int]        =		False

	A__							:    Dict        =		MBartaaTokenizer(lowercase_					)
	tokenizer.save_pretrained(lowercase_					)

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

	A__							:    Union[str, Any]        =		tokenizer.eos_token_id
	A__							:    Tuple        =		250004
	A__							:    int        =		tokenizer.eos_token_id

	A__							:    Any        =		SpeechEncoderDecoderConfig.from_dict(lowercase_					)

	hf_wavavec.save_pretrained(lowercase_					)
	feature_extractor.save_pretrained(lowercase_					)


if __name__ == "__main__":
	A_       : int         =			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=25_0004, type=int, help='`decoder_start_token_id` of model config')

	A_       : List[str]         =			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,
	)






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



