



"""simple docstring"""






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()
lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''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''',
}
lowerCAmelCase__      =    [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 	snake_case_  (				A_ :		Tuple,    A_ :		List[str],    A_ :		str,    A_ :		List[str],    A_ :		Optional[Any]						):


       '''simple docstring'''





       for attribute in key.split('''.'''						):
              _lowerCamelCase						:    List[Any]         =			getattr(A_,    A_						)

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

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






def 	snake_case_  (				A_ :		int,    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			[]
       _lowerCamelCase						:    int         =			fairseq_model.state_dict()

       _lowerCamelCase						:    Optional[int]         =			hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _lowerCamelCase						:    int         =			False
              if "conv_layers" in name:
                     load_conv_layer(
                         A_,    A_,    A_,    A_,    hf_model.config.feat_extract_norm == '''group''',    )
                     _lowerCamelCase						:    Any         =			True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                                   _lowerCamelCase						:    Optional[Any]         =			True
                                   if "*" in mapped_key:
                                          _lowerCamelCase						:    str         =			name.split(A_						)[0].split('''.'''						)[-2]
                                          _lowerCamelCase						:    Dict         =			mapped_key.replace('''*''',    A_						)
                                   if "weight_g" in name:
                                          _lowerCamelCase						:    Optional[Any]         =			'''weight_g'''
                                   elif "weight_v" in name:
                                          _lowerCamelCase						:    List[Any]         =			'''weight_v'''
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          _lowerCamelCase						:    List[str]         =			'''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _lowerCamelCase						:    Tuple         =			'''weight'''
                                   else:
                                          _lowerCamelCase						:    Dict         =			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 	snake_case_  (				A_ :		Any,    A_ :		Dict,    A_ :		Optional[int],    A_ :		Dict,    A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			full_name.split('''conv_layers.'''						)[-1]
       _lowerCamelCase						:    str         =			name.split('''.'''						)
       _lowerCamelCase						:    Tuple         =			int(items[0]						)
       _lowerCamelCase						:    Dict         =			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						:    str         =			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						:    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."
                     )
                     _lowerCamelCase						:    List[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.'''
                     )
                     _lowerCamelCase						:    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(A_						)






@torch.no_grad()
def 	snake_case_  (				A_ :		Any,    A_ :		Any,    A_ :		Any=None						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			torch.load(A_						)
       _lowerCamelCase						:    int         =			WavLMConfigOrig(checkpoint['''cfg''']						)
       _lowerCamelCase						:    Any         =			WavLMOrig(A_						)
       model.load_state_dict(checkpoint['''model''']						)
       model.eval()

       if config_path is not None:
              _lowerCamelCase						:    Dict         =			WavLMConfig.from_pretrained(A_						)
       else:
              _lowerCamelCase						:    Optional[int]         =			WavLMConfig()

       _lowerCamelCase						:    Union[str, Any]         =			WavLMModel(A_						)

       recursively_load_weights(A_,    A_						)

       hf_wavlm.save_pretrained(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    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''')
   lowerCAmelCase__      =    parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from transformers.modeling_outputs import BaseModelOutput

   from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCAmelCase__      =    logging.get_logger(__name__)






@add_end_docstrings(_lowercase)
class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Any						, **__lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)

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

         requires_backends(self						, '''vision'''		)
         self.check_model_type(__lowerCAmelCase		)




  def __call__(			self					:						Dict						, __lowerCAmelCase					:						Union[str, "Image.Image", List[Dict[str, Any]]]						, __lowerCAmelCase					:						Union[str, List[str]] = None						, **__lowerCAmelCase					:						int						, ):






         """simple docstring"""






         if "text_queries" in kwargs:
                _lowerCamelCase						:    List[Any]         =			kwargs.pop('''text_queries'''		)

         if isinstance(__lowerCAmelCase						, (str, Image.Image)		):
                _lowerCamelCase						:    Optional[int]         =			{'''image''': image, '''candidate_labels''': candidate_labels}
         else:
                _lowerCamelCase						:    List[Any]         =			image
         _lowerCamelCase						:    List[str]         =			super().__call__(__lowerCAmelCase						, **__lowerCAmelCase		)
         return results




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, **__lowerCAmelCase					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			{}
         if "threshold" in kwargs:
                _lowerCamelCase						:    Optional[Any]         =			kwargs['''threshold''']
         if "top_k" in kwargs:
                _lowerCamelCase						:    int         =			kwargs['''top_k''']
         return {}, {}, postprocess_params




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			load_image(inputs['''image''']		)
         _lowerCamelCase						:    Optional[Any]         =			inputs['''candidate_labels''']
         if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                _lowerCamelCase						:    int         =			candidate_labels.split(''','''		)

         _lowerCamelCase						:    Tuple         =			torch.tensor([[image.height, image.width]]						, dtype=torch.intaa		)
         for i, candidate_label in enumerate(__lowerCAmelCase		):
                _lowerCamelCase						:    Any         =			self.tokenizer(__lowerCAmelCase						, return_tensors=self.framework		)
                _lowerCamelCase						:    Optional[Any]         =			self.image_processor(__lowerCAmelCase						, return_tensors=self.framework		)
                yield {
                    "is_last": i == len(__lowerCAmelCase		) - 1,
                    "target_size": target_size,
                    "candidate_label": candidate_label,
                    **text_inputs,
                    **image_features,
                }




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			model_inputs.pop('''target_size'''		)
         _lowerCamelCase						:    List[Any]         =			model_inputs.pop('''candidate_label'''		)
         _lowerCamelCase						:    Dict         =			model_inputs.pop('''is_last'''		)

         _lowerCamelCase						:    str         =			self.model(**__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
         return model_outputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Union[str, Any]=0.1						, __lowerCAmelCase					:						Optional[Any]=None		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			[]
         for model_output in model_outputs:
                _lowerCamelCase						:    Any         =			model_output['''candidate_label''']
                _lowerCamelCase						:    Union[str, Any]         =			BaseModelOutput(__lowerCAmelCase		)
                _lowerCamelCase						:    Tuple         =			self.image_processor.post_process_object_detection(
                    outputs=__lowerCAmelCase						, threshold=__lowerCAmelCase						, target_sizes=model_output['''target_size''']		)[0]

                for index in outputs["scores"].nonzero():
                       _lowerCamelCase						:    Tuple         =			outputs['''scores'''][index].item()
                       _lowerCamelCase						:    Optional[Any]         =			self._get_bounding_box(outputs['''boxes'''][index][0]		)

                       _lowerCamelCase						:    Optional[Any]         =			{'''score''': score, '''label''': label, '''box''': box}
                       results.append(__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			sorted(__lowerCAmelCase						, key=lambda __lowerCAmelCase		: x["score"]						, reverse=__lowerCAmelCase		)
         if top_k:
                _lowerCamelCase						:    Dict         =			results[:top_k]

         return results




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						"torch.Tensor"		):






         """simple docstring"""






         if self.framework != "pt":
                raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''		)
         _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			box.int().tolist()
         _lowerCamelCase						:    Union[str, Any]         =			{
             '''xmin''': xmin,
             '''ymin''': ymin,
             '''xmax''': xmax,
             '''ymax''': ymax,
         }
         return bbox


