






'''simple docstring'''

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCamelCase      					={
    "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",
}
UpperCamelCase      					=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]



def 				snake_case       (  a_				:			List[str]     , a_				:			Optional[int]     , a_				:			int     , a_				:			Tuple     , a_				:			Any						)							->					str:





      """simple docstring"""

      for attribute in key.split("""."""						):
            UpperCamelCase_					: List[Any]            =				getattr(a_     , a_						)

      if weight_type is not None:
            UpperCamelCase_					: List[str]            =				getattr(a_     , a_						).shape
      else:
            UpperCamelCase_					: Dict            =				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":
            UpperCamelCase_					: Union[str, Any]            =				value
      elif weight_type == "weight_g":
            UpperCamelCase_					: int            =				value
      elif weight_type == "weight_v":
            UpperCamelCase_					: List[Any]            =				value
      elif weight_type == "bias":
            UpperCamelCase_					: List[str]            =				value
      else:
            UpperCamelCase_					: Dict            =				value

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



def 				snake_case       (  a_				:			List[str]     , a_				:			Tuple						)							->					Dict:





      """simple docstring"""

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

      UpperCamelCase_					: Any            =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase_					: Any            =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      a_     , a_     , a_     , a_     , hf_model.config.feat_extract_norm == """group"""     , )
                  UpperCamelCase_					: List[str]            =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase_					: 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
                              UpperCamelCase_					: Dict            =				True
                              if "*" in mapped_key:
                                    UpperCamelCase_					: Optional[int]            =				name.split(a_						)[0].split("""."""						)[-2]
                                    UpperCamelCase_					: List[str]            =				mapped_key.replace("""*"""     , a_						)
                              if "weight_g" in name:
                                    UpperCamelCase_					: str            =				"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase_					: Optional[Any]            =				"""weight_v"""
                              elif "bias" in name:
                                    UpperCamelCase_					: Union[str, Any]            =				"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCamelCase_					: List[str]            =				"""weight"""
                              else:
                                    UpperCamelCase_					: Union[str, Any]            =				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_				:			Optional[int]     , a_				:			List[str]     , a_				:			Tuple     , a_				:			Tuple     , a_				:			List[str]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Any            =				full_name.split("""conv_layers."""						)[-1]
      UpperCamelCase_					: Optional[int]            =				name.split("""."""						)
      UpperCamelCase_					: Dict            =				int(items[0]						)
      UpperCamelCase_					: 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."						)
                  UpperCamelCase_					: Union[str, 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."						)
                  UpperCamelCase_					: Union[str, 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."						)
                  UpperCamelCase_					: Any            =				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."						)
                  UpperCamelCase_					: 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_				:			Optional[int]     , a_				:			Dict     , a_				:			List[str]=None     , a_				:			str=None     , a_				:			Optional[int]=True						)							->					Tuple:





      """simple docstring"""

      if config_path is not None:
            UpperCamelCase_					: Union[str, Any]            =				UniSpeechSatConfig.from_pretrained(a_						)
      else:
            UpperCamelCase_					: Union[str, Any]            =				UniSpeechSatConfig()

      UpperCamelCase_					: Optional[Any]            =				""""""

      if is_finetuned:
            UpperCamelCase_					: int            =				UniSpeechSatForCTC(a_						)
      else:
            UpperCamelCase_					: Tuple            =				UniSpeechSatForPreTraining(a_						)

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

      recursively_load_weights(a_     , a_						)

      hf_wavavec.save_pretrained(a_						)


if __name__ == "__main__":
      UpperCamelCase      					=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"
      )
      UpperCamelCase      					=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
      )














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






