

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
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    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		Union[str, Any]        =		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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		self.dummy_uncond_unet
      snake_case							:		Union[str, Any]        =		ScoreSdeVeScheduler()

      snake_case							:		int        =		ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_    )
      sde_ve.to(SCREAMING_SNAKE_CASE_    )
      sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Union[str, Any]        =		torch.manual_seed(0    )
      snake_case							:		Dict        =		sde_ve(num_inference_steps=2   ,output_type="""numpy"""   ,generator=SCREAMING_SNAKE_CASE_    ).images

      snake_case							:		Tuple        =		torch.manual_seed(0    )
      snake_case							:		str        =		sde_ve(num_inference_steps=2   ,output_type="""numpy"""   ,generator=SCREAMING_SNAKE_CASE_   ,return_dict=SCREAMING_SNAKE_CASE_    )[
          0
      ]

      snake_case							:		Union[str, Any]        =		image[0, -3:, -3:, -1]
      snake_case							:		Tuple        =		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      snake_case							:		Tuple        =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2







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





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		"""google/ncsnpp-church-256"""
      snake_case							:		Any        =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[str]        =		ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Tuple        =		ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_    )
      sde_ve.to(SCREAMING_SNAKE_CASE_    )
      sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Union[str, Any]        =		torch.manual_seed(0    )
      snake_case							:		str        =		sde_ve(num_inference_steps=10   ,output_type="""numpy"""   ,generator=SCREAMING_SNAKE_CASE_    ).images

      snake_case							:		Union[str, Any]        =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 256, 256, 3)

      snake_case							:		Tuple        =		np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2




"""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()
__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		for attribute in key.split("""."""  ):
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  )

		if weight_type is not None:
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  ).shape
		else:
				lowerCAmelCase__   = 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":
				lowerCAmelCase__   = value
		elif weight_type == "weight_g":
				lowerCAmelCase__   = value
		elif weight_type == "weight_v":
				lowerCAmelCase__   = value
		elif weight_type == "bias":
				lowerCAmelCase__   = value
		else:
				lowerCAmelCase__   = value

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		lowerCAmelCase__   = fairseq_model.state_dict()

		lowerCAmelCase__   = hf_model.feature_extractor
		lowerCAmelCase__   = hf_model.adapter

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

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""conv_layers."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )
		lowerCAmelCase__   = int(items[0]  )
		lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""adaptor."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )

		if items[1].isdigit():
				lowerCAmelCase__   = int(items[1]  )
		else:
				lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
		elif isinstance(lowerCamelCase__      ,	lowerCamelCase__  ):
				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."""
						lowerCAmelCase__   = 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."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = emb.weight.shape
		lowerCAmelCase__   = nn.Linear(lowerCamelCase__      ,	lowerCamelCase__      ,	bias=lowerCamelCase__  )
		lowerCAmelCase__   = emb.weight.data
		return lin_layer







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = WavaVecaConfig.from_pretrained(
		    lowerCamelCase__      ,	add_adapter=lowerCamelCase__      ,	adapter_stride=lowerCamelCase__      ,	adapter_kernel_size=lowerCamelCase__      ,	use_auth_token=lowerCamelCase__      ,	output_hidden_size=lowerCamelCase__      ,	)
		lowerCAmelCase__   = MBartConfig.from_pretrained(lowerCamelCase__  )

		# load model
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 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,
		    }      ,	)
		lowerCAmelCase__   = model[0].eval()

		# load feature extractor
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__      ,	use_auth_token=lowerCamelCase__  )

		# set weights for wav2vec2 encoder
		lowerCAmelCase__   = WavaVecaModel(lowerCamelCase__  )

		recursively_load_weights_wavaveca(model.encoder      ,	lowerCamelCase__  )

		# load decoder weights
		lowerCAmelCase__   = MBartForCausalLM(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,	strict=lowerCamelCase__  )
		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}"""  )

		lowerCAmelCase__   = SpeechEncoderDecoderModel(encoder=lowerCamelCase__      ,	decoder=lowerCamelCase__  )
		lowerCAmelCase__   = False

		lowerCAmelCase__   = MBartaaTokenizer(lowerCamelCase__  )
		tokenizer.save_pretrained(lowerCamelCase__  )

		lowerCAmelCase__   = hf_wavavec.config.to_dict()
		lowerCAmelCase__   = tokenizer.pad_token_id
		lowerCAmelCase__   = tokenizer.bos_token_id
		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = """mbart50"""
		lowerCAmelCase__   = """wav2vec2"""

		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = 25_0004
		lowerCAmelCase__   = tokenizer.eos_token_id

		lowerCAmelCase__   = SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

		hf_wavavec.save_pretrained(lowerCamelCase__  )
		feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				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=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")

			__lowerCAmelCase     :				Union[str, Any]    				=    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,
			)




