




"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase				:     Tuple					=				logging.get_logger(__name__)

lowerCAmelCase				:     Optional[int]					=				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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				:     str					=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   int:
			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

			if hf_shape != value.shape:
						raise ValueError(
						    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
			elif weight_type == "running_mean":
						lowerCamelCase      =   value
			elif weight_type == "running_var":
						lowerCamelCase      =   value
			elif weight_type == "num_batches_tracked":
						lowerCamelCase      =   value
			elif weight_type == "inv_freq":
						lowerCamelCase      =   value
			else:
						lowerCamelCase      =   value

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



def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   List[Any]:
			lowerCamelCase      =   []
			lowerCamelCase      =   fairseq_model.state_dict()

			lowerCamelCase      =   hf_model.wavaveca_conformer.feature_extractor

			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
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase      =   """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												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 "pos_bias_u" in name:
																		lowerCamelCase      =   None
															elif "pos_bias_v" in name:
																		lowerCamelCase      =   None
															elif "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:
																		# TODO: don't match quantizer.weight_proj
																		lowerCamelCase      =   """weight"""
															elif "running_mean" in name:
																		lowerCamelCase      =   """running_mean"""
															elif "inv_freq" in name:
																		lowerCamelCase      =   """inv_freq"""
															elif "running_var" in name:
																		lowerCamelCase      =   """running_var"""
															elif "num_batches_tracked" in name:
																		lowerCamelCase      =   """num_batches_tracked"""
															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        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   Dict:
			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:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    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:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[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__							)



@torch.no_grad()
def        a__					(							snake_case__			,					snake_case__			,					snake_case__=None			,					snake_case__=None			,					snake_case__=True							)		->   int:
			if config_path is not None:
						lowerCamelCase      =   WavaVecaConformerConfig.from_pretrained(lowerCamelCase__			,					hidden_act="""swish"""							)
			else:
						lowerCamelCase      =   WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						lowerCamelCase      =   """rotary"""

			if is_finetuned:
						if dict_path:
									lowerCamelCase      =   Dictionary.load(lowerCamelCase__							)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase      =   target_dict.pad_index
									lowerCamelCase      =   target_dict.bos_index
									lowerCamelCase      =   target_dict.eos_index
									lowerCamelCase      =   len(target_dict.symbols							)
									lowerCamelCase      =   os.path.join(lowerCamelCase__			,					"""vocab.json"""							)
									if not os.path.isdir(lowerCamelCase__							):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase__							)							)
												return
									os.makedirs(lowerCamelCase__			,					exist_ok=lowerCamelCase__							)
									lowerCamelCase      =   target_dict.indices

									# fairseq has the <pad> and <s> switched
									lowerCamelCase      =   0
									lowerCamelCase      =   1
									with open(lowerCamelCase__			,					"""w"""			,					encoding="""utf-8"""							) as vocab_handle:
												json.dump(lowerCamelCase__			,					lowerCamelCase__							)
									lowerCamelCase      =   WavaVecaCTCTokenizer(
									    lowerCamelCase__			,					unk_token=target_dict.unk_word			,					pad_token=target_dict.pad_word			,					bos_token=target_dict.bos_word			,					eos_token=target_dict.eos_word			,					word_delimiter_token="""|"""			,					do_lower_case=lowerCamelCase__			,					)
									lowerCamelCase      =   True if config.feat_extract_norm == """layer""" else False
									lowerCamelCase      =   WavaVecaFeatureExtractor(
									    feature_size=1			,					sampling_rate=1_60_00			,					padding_value=0			,					do_normalize=lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					)
									lowerCamelCase      =   WavaVecaProcessor(feature_extractor=lowerCamelCase__			,					tokenizer=lowerCamelCase__							)
									processor.save_pretrained(lowerCamelCase__							)

						lowerCamelCase      =   WavaVecaConformerForCTC(lowerCamelCase__							)
			else:
						lowerCamelCase      =   WavaVecaConformerForPreTraining(lowerCamelCase__							)

			if is_finetuned:
						lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
			else:
						lowerCamelCase      =   argparse.Namespace(task="""audio_pretraining"""							)
						lowerCamelCase      =   fairseq.tasks.setup_task(lowerCamelCase__							)

						lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,					task=lowerCamelCase__							)

			lowerCamelCase      =   model[0].eval()

			recursively_load_weights(lowerCamelCase__			,					lowerCamelCase__			,					not is_finetuned							)

			hf_wavavec.save_pretrained(lowerCamelCase__							)


if __name__ == "__main__":
					lowerCAmelCase				:     List[str]					=				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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase				:     int					=				parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
					assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
					assert len(self.x.shape					) == len(self.y.shape					) == len(self.z.shape					) == len(self.origin.shape					) == 2



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return torch.from_numpy(np.array([self.width, self.height]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			torch.Tensor:
					lowerCamelCase_         =     torch.arange(self.height * self.width					)
					lowerCamelCase_         =     torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(lowercase		,    self.width		,    rounding_mode="trunc"					),
					    ]		,    axis=1		,    )
					return coords



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_      ,       *lowerCamelCase_         =     self.shape
					lowerCamelCase_         =     int(np.prod(lowercase					)					)

					lowerCamelCase_         =     self.get_image_coords()
					lowerCamelCase_         =     torch.broadcast_to(coords.unsqueeze(0					)		,    [batch_size * inner_batch_size, *coords.shape]					)
					lowerCamelCase_         =     self.get_camera_rays(lowercase					)

					lowerCamelCase_         =     rays.view(lowercase		,    inner_batch_size * self.height * self.width		,    2		,    3					)

					return rays



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			torch.Tensor:
					lowerCamelCase_      ,       *lowerCamelCase_      ,       lowerCamelCase_         =     coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					lowerCamelCase_         =     coords.view(lowercase		,    -1		,    2					)

					lowerCamelCase_         =     self.resolution()
					lowerCamelCase_         =     self.fov()

					lowerCamelCase_         =     (flat.float() / (res - 1)) * 2 - 1
					lowerCamelCase_         =     fracs * torch.tan(fov / 2					)

					lowerCamelCase_         =     fracs.view(lowercase		,    -1		,    2					)
					lowerCamelCase_         =     (
					    self.z.view(lowercase		,    1		,    3					)
					    + self.x.view(lowercase		,    1		,    3					) * fracs[:, :, :1]
					    + self.y.view(lowercase		,    1		,    3					) * fracs[:, :, 1:]
					)
					lowerCamelCase_         =     directions / directions.norm(dim=-1		,    keepdim=lowercase					)
					lowerCamelCase_         =     torch.stack(
					    [
					        torch.broadcast_to(self.origin.view(lowercase		,    1		,    3					)		,    [batch_size, directions.shape[1], 3]					),
					        directions,
					    ]		,    dim=2		,    )
					return rays.view(lowercase		,    *lowercase		,    2		,    3					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			"DifferentiableProjectiveCamera":
					assert width * self.height == height * self.width, "The aspect ratio should not change."
					return DifferentiableProjectiveCamera(
					    origin=self.origin		,    x=self.x		,    y=self.y		,    z=self.z		,    width=lowercase		,    height=lowercase		,    x_fov=self.x_fov		,    y_fov=self.y_fov		,    )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				for theta in np.linspace(0      ,					2 * np.pi      ,					num=2_0       ):
								lowerCamelCase_         =     np.array([np.sin(lowerCamelCase__       ), np.cos(lowerCamelCase__       ), -0.5]       )
								z /= np.sqrt(np.sum(z**2       )       )
								lowerCamelCase_         =     -z * 4
								lowerCamelCase_         =     np.array([np.cos(lowerCamelCase__       ), -np.sin(lowerCamelCase__       ), 0.0]       )
								lowerCamelCase_         =     np.cross(lowerCamelCase__      ,					lowerCamelCase__       )
								origins.append(lowerCamelCase__       )
								xs.append(lowerCamelCase__       )
								ys.append(lowerCamelCase__       )
								zs.append(lowerCamelCase__       )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					x=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					y=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					z=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					width=lowerCamelCase__      ,					height=lowerCamelCase__      ,					x_fov=0.7      ,					y_fov=0.7      ,					shape=(1, len(lowerCamelCase__       ))      ,					)


