




from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__A        =    logging.get_logger(__name__)







@add_end_docstrings(__magic_name__ )
class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def __init__(      self			,						**lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__							)
							requires_backends(self			,						'vision'							)
							requires_backends(self			,						'torch'							)

							if self.framework != "pt":
											raise ValueError(f"""The {self.__class__} is only available in PyTorch."""							)

							self.check_model_type(lowerCamelCase__							)


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    {}
							__lowerCamelCase		       =    {}
							__lowerCamelCase		       =    {}
							# preprocess args
							if "points_per_batch" in kwargs:
											__lowerCamelCase		       =    kwargs['points_per_batch']
							if "points_per_crop" in kwargs:
											__lowerCamelCase		       =    kwargs['points_per_crop']
							if "crops_n_layers" in kwargs:
											__lowerCamelCase		       =    kwargs['crops_n_layers']
							if "crop_overlap_ratio" in kwargs:
											__lowerCamelCase		       =    kwargs['crop_overlap_ratio']
							if "crop_n_points_downscale_factor" in kwargs:
											__lowerCamelCase		       =    kwargs['crop_n_points_downscale_factor']
							# postprocess args
							if "pred_iou_thresh" in kwargs:
											__lowerCamelCase		       =    kwargs['pred_iou_thresh']
							if "stability_score_offset" in kwargs:
											__lowerCamelCase		       =    kwargs['stability_score_offset']
							if "mask_threshold" in kwargs:
											__lowerCamelCase		       =    kwargs['mask_threshold']
							if "stability_score_thresh" in kwargs:
											__lowerCamelCase		       =    kwargs['stability_score_thresh']
							if "crops_nms_thresh" in kwargs:
											__lowerCamelCase		       =    kwargs['crops_nms_thresh']
							if "output_rle_mask" in kwargs:
											__lowerCamelCase		       =    kwargs['output_rle_mask']
							if "output_bboxes_mask" in kwargs:
											__lowerCamelCase		       =    kwargs['output_bboxes_mask']
							return preprocess_kwargs, forward_params, postprocess_kwargs


			def __call__(      self			,						lowerCamelCase__			,						*lowerCamelCase__			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return super().__call__(lowerCamelCase__			,						*lowerCamelCase__			,						num_workers=lowerCamelCase__			,						batch_size=lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=64			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = 512 / 1_500			,						lowerCamelCase__ = 32			,						lowerCamelCase__ = 1			,						)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    load_image(lowerCamelCase__							)
							__lowerCamelCase		       =    self.image_processor.size['longest_edge']
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.generate_crop_boxes(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    self.image_processor(images=lowerCamelCase__			,						return_tensors='pt'							)

							with self.device_placement():
											if self.framework == "pt":
															__lowerCamelCase		       =    self.get_inference_context()
															with inference_context():
																			__lowerCamelCase		       =    self._ensure_tensor_on_device(lowerCamelCase__			,						device=self.device							)
																			__lowerCamelCase		       =    self.model.get_image_embeddings(model_inputs.pop('pixel_values'							)							)
																			__lowerCamelCase		       =    image_embeddings

							__lowerCamelCase		       =    grid_points.shape[1]
							__lowerCamelCase		       =    points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
											raise ValueError(
											    'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
											    'To return all points at once, set points_per_batch to None'							)

							for i in range(0			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    grid_points[:, i : i + points_per_batch, :, :]
											__lowerCamelCase		       =    input_labels[:, i : i + points_per_batch]
											__lowerCamelCase		       =    i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=0.88			,						lowerCamelCase__=0.95			,						lowerCamelCase__=0			,						lowerCamelCase__=1			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    model_inputs.pop('input_boxes'							)
							__lowerCamelCase		       =    model_inputs.pop('is_last'							)
							__lowerCamelCase		       =    model_inputs.pop('original_sizes'							).tolist()
							__lowerCamelCase		       =    model_inputs.pop('reshaped_input_sizes'							).tolist()

							__lowerCamelCase		       =    self.model(**lowerCamelCase__							)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							__lowerCamelCase		       =    model_outputs['pred_masks']
							__lowerCamelCase		       =    self.image_processor.post_process_masks(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						binarize=lowerCamelCase__							)
							__lowerCamelCase		       =    model_outputs['iou_scores']
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.filter_masks(
							    masks[0]			,						iou_scores[0]			,						original_sizes[0]			,						input_boxes[0]			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=False			,						lowerCamelCase__=False			,						lowerCamelCase__=0.7			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							for model_output in model_outputs:
											all_scores.append(model_output.pop('iou_scores'							)							)
											all_masks.extend(model_output.pop('masks'							)							)
											all_boxes.append(model_output.pop('boxes'							)							)

							__lowerCamelCase		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.post_process_for_mask_generation(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

							__lowerCamelCase		       =    defaultdict(lowerCamelCase__							)
							for output in model_outputs:
											for k, v in output.items():
															extra[k].append(lowerCamelCase__							)

							__lowerCamelCase		       =    {}
							if output_rle_mask:
											__lowerCamelCase		       =    rle_mask

							if output_bboxes_mask:
											__lowerCamelCase		       =    bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}










import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

	if weight_type is not None:
		UpperCamelCase     :Optional[int]          =	getattr(__magic_name__	,			__magic_name__					).shape
	else:
		UpperCamelCase     :Optional[int]          =	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     :int          =	value
	elif weight_type == "bias":
		UpperCamelCase     :List[Any]          =	value
	else:
		UpperCamelCase     :Any          =	value

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCamelCase     :str          =	False
		if "conv_layers" in name:
			load_conv_layer(
			    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			hf_model.config.feat_extract_norm == """group"""	,			)
			UpperCamelCase     :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]:
					UpperCamelCase     :Optional[int]          =	True
					if "*" in mapped_key:
						UpperCamelCase     :List[Any]          =	name.split(__magic_name__					)[0].split("""."""					)[-2]
						UpperCamelCase     :int          =	mapped_key.replace("""*"""	,			__magic_name__					)
					if "weight_g" in name:
						UpperCamelCase     :List[Any]          =	"""weight_g"""
					elif "weight_v" in name:
						UpperCamelCase     :List[Any]          =	"""weight_v"""
					elif "bias" in name and "relative_attention_bias" not in name:
						UpperCamelCase     :Any          =	"""bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCamelCase     :List[str]          =	"""weight"""
					else:
						UpperCamelCase     :Optional[int]          =	None

					set_recursively(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)
				continue
		if not is_used:
			unused_weights.append(__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



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

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						Union[str, Any]      =      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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
