

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor"""]
    __lowerCamelCase						:	Tuple														=					"""SamImageProcessor"""





    def __init__(				self						,       a):
         super().__init__(a)
         lowercase__   :       Dict				=	self.image_processor
         lowercase__   :       str				=	-10
         lowercase__   :       str				=	self.image_processor.size['longest_edge']





    def __call__(				self						,       a=None						,       a=None						,       a=None						,       a=None						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.image_processor(
             a						,       return_tensors=a						,       **a						,       )

         # pop arguments that are not used in the foward but used nevertheless
         lowercase__   :       str				=	encoding_image_processor['original_sizes']

         if hasattr(a						,       'numpy'):  # Checks if Torch or TF tensor
              lowercase__   :       Tuple				=	original_sizes.numpy()

         lowercase__				,			lowercase__				,			lowercase__   :       Optional[Any]				=	self._check_and_preprocess_points(
             input_points=a						,       input_labels=a						,       input_boxes=a						,       )

         lowercase__   :       List[str]				=	self._normalize_and_convert(
             a						,       a						,       input_points=a						,       input_labels=a						,       input_boxes=a						,       return_tensors=a						,       )

         return encoding_image_processor





    def 				snake_case_    (				self						,       a						,       a						,       a=None						,       a=None						,       a=None						,       a="pt"						,       ):
         if input_points is not None:
              if len(a) != len(a):
                   lowercase__   :       Union[str, Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       original_sizes[0]) for point in input_points
                   ]
              else:
                   lowercase__   :       List[Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       a)
                       for point, original_size in zip(a						,       a)
                   ]
              # check that all arrays have the same shape
              if not all(point.shape == input_points[0].shape for point in input_points):
                   if input_labels is not None:
                        lowercase__				,			lowercase__   :       List[str]				=	self._pad_points_and_labels(a						,       a)

              lowercase__   :       Union[str, Any]				=	np.array(a)

         if input_labels is not None:
              lowercase__   :       Union[str, Any]				=	np.array(a)

         if input_boxes is not None:
              if len(a) != len(a):
                   lowercase__   :       Union[str, Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       original_sizes[0]						,       is_bounding_box=a)
                       for box in input_boxes
                   ]
              else:
                   lowercase__   :       Tuple				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       a						,       is_bounding_box=a)
                       for box, original_size in zip(a						,       a)
                   ]
              lowercase__   :       Optional[Any]				=	np.array(a)

         if input_boxes is not None:
              if return_tensors == "pt":
                   lowercase__   :       Union[str, Any]				=	torch.from_numpy(a)
                   # boxes batch size of 1 by default
                   lowercase__   :       Dict				=	input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
              elif return_tensors == "tf":
                   lowercase__   :       Dict				=	tf.convert_to_tensor(a)
                   # boxes batch size of 1 by default
                   lowercase__   :       str				=	tf.expand_dims(a						,       1) if len(input_boxes.shape) != 3 else input_boxes
              encoding_image_processor.update({'input_boxes': input_boxes})
         if input_points is not None:
              if return_tensors == "pt":
                   lowercase__   :       int				=	torch.from_numpy(a)
                   # point batch size of 1 by default
                   lowercase__   :       Union[str, Any]				=	input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
              elif return_tensors == "tf":
                   lowercase__   :       Optional[int]				=	tf.convert_to_tensor(a)
                   # point batch size of 1 by default
                   lowercase__   :       str				=	tf.expand_dims(a						,       1) if len(input_points.shape) != 4 else input_points
              encoding_image_processor.update({'input_points': input_points})
         if input_labels is not None:
              if return_tensors == "pt":
                   lowercase__   :       int				=	torch.from_numpy(a)
                   # point batch size of 1 by default
                   lowercase__   :       str				=	input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
              elif return_tensors == "tf":
                   lowercase__   :       Optional[int]				=	tf.convert_to_tensor(a)
                   # point batch size of 1 by default
                   lowercase__   :       List[str]				=	tf.expand_dims(a						,       1) if len(input_labels.shape) != 3 else input_labels
              encoding_image_processor.update({'input_labels': input_labels})

         return encoding_image_processor





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Tuple				=	max([point.shape[0] for point in input_points])
         lowercase__   :       Optional[int]				=	[]
         for i, point in enumerate(a):
              if point.shape[0] != expected_nb_points:
                   lowercase__   :       Optional[Any]				=	np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]						,       axis=0)
                   lowercase__   :       Optional[Any]				=	np.append(input_labels[i]						,       [self.point_pad_value])
              processed_input_points.append(a)
         lowercase__   :       int				=	processed_input_points
         return input_points, input_labels





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a=False):
         lowercase__				,			lowercase__   :       Tuple				=	original_size
         lowercase__				,			lowercase__   :       Optional[int]				=	self.image_processor._get_preprocess_shape(a						,       longest_edge=a)
         lowercase__   :       Any				=	deepcopy(a).astype(a)

         if is_bounding_box:
              lowercase__   :       int				=	coords.reshape(-1						,       2						,       2)

         lowercase__   :       str				=	coords[..., 0] * (new_w / old_w)
         lowercase__   :       List[Any]				=	coords[..., 1] * (new_h / old_h)

         if is_bounding_box:
              lowercase__   :       int				=	coords.reshape(-1						,       4)

         return coords





    def 				snake_case_    (				self						,       a=None						,       a=None						,       a=None						,       ):
         if input_points is not None:
              if hasattr(a						,       'numpy'):  # Checks for TF or Torch tensor
                   lowercase__   :       List[str]				=	input_points.numpy().tolist()

              if not isinstance(a						,       a) or not isinstance(input_points[0]						,       a):
                   raise ValueError('Input points must be a list of list of floating points.')
              lowercase__   :       Tuple				=	[np.array(a) for input_point in input_points]
         else:
              lowercase__   :       List[Any]				=	None

         if input_labels is not None:
              if hasattr(a						,       'numpy'):
                   lowercase__   :       Any				=	input_labels.numpy().tolist()

              if not isinstance(a						,       a) or not isinstance(input_labels[0]						,       a):
                   raise ValueError('Input labels must be a list of list integers.')
              lowercase__   :       Union[str, Any]				=	[np.array(a) for label in input_labels]
         else:
              lowercase__   :       List[Any]				=	None

         if input_boxes is not None:
              if hasattr(a						,       'numpy'):
                   lowercase__   :       List[str]				=	input_boxes.numpy().tolist()

              if (
                  not isinstance(a						,       a)
                  or not isinstance(input_boxes[0]						,       a)
                  or not isinstance(input_boxes[0][0]						,       a)
              ):
                   raise ValueError('Input boxes must be a list of list of list of floating points.')
              lowercase__   :       Optional[Any]				=	[np.array(a).astype(np.floataa) for box in input_boxes]
         else:
              lowercase__   :       Optional[int]				=	None

         return input_points, input_labels, input_boxes





    @property
    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.image_processor.model_input_names
         return list(dict.fromkeys(a))





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.image_processor.post_process_masks(*a						,       **a)




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
snake_case_        =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     for attribute in key.split('.'      ):
          lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

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

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	[]
     lowercase__   :       Union[str, Any]				=	fairseq_model.state_dict()

     lowercase__   :       Optional[int]				=	hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__   :       Tuple				=	False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					hf_model.config.feat_extract_norm == 'group'     ,					)
               lowercase__   :       Optional[int]				=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase__   :       List[str]				=	'unispeech_sat.' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'      )[:-1]      ) != key):
                              # special case since naming is very similar
                              continue
                         lowercase__   :       Tuple				=	True
                         if "*" in mapped_key:
                              lowercase__   :       Any				=	name.split(SCREAMING_SNAKE_CASE_      )[0].split('.'      )[-2]
                              lowercase__   :       Optional[Any]				=	mapped_key.replace('*'     ,					SCREAMING_SNAKE_CASE_      )
                         if "weight_g" in name:
                              lowercase__   :       int				=	'weight_g'
                         elif "weight_v" in name:
                              lowercase__   :       Any				=	'weight_v'
                         elif "bias" in name:
                              lowercase__   :       str				=	'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__   :       Union[str, Any]				=	'weight'
                         else:
                              lowercase__   :       Union[str, Any]				=	None
                         set_recursively(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_      )

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	full_name.split('conv_layers.'      )[-1]
     lowercase__   :       Dict				=	name.split('.'      )
     lowercase__   :       List[str]				=	int(items[0]      )
     lowercase__   :       Any				=	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."""      )
               lowercase__   :       List[Any]				=	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."""      )
               lowercase__   :       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:
               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[layer_id].layer_norm.bias.data.shape} was found."""      )
               lowercase__   :       str				=	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[layer_id].layer_norm.weight.data.shape} was found."""      )
               lowercase__   :       int				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE_      )

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   int=True      ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__   :       Any				=	UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	UniSpeechSatConfig()

     lowercase__   :       str				=	''

     if is_finetuned:
          lowercase__   :       Any				=	UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Optional[Any]				=	UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_      )

     lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]     ,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     lowercase__   :       List[str]				=	model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       snake_case_        =			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'''
       )
       snake_case_        =			parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

